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sarvamai/sarvam-105b · Hugging Face
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https://cdn-uploads.huggingface.co/production/uploads/60270a7c32856987162c641a/j1SbA6V6HixP1-6H0N41q.png

Want a smaller model? Download [Sarvam-30B](https://huggingface.co/sarvamai/sarvam-30b/)!

## #index

 Index

- [Introduction](#introduction)

- [Architecture](#architecture)

- [Benchmarks](#benchmarks)
Knowledge & Coding

- Reasoning & Math

- Agentic

- [Inference](#inference)
Hugging Face

- vLLM

- [Footnote](#footnote)

- [Citation](#citation)

## #introduction

 Introduction

Sarvam-105B is an advanced Mixture-of-Experts (MoE) model with 10.3B active parameters, designed for superior performance across a wide range of complex tasks. It is highly optimized for complex reasoning, with particular strength in agentic tasks, mathematics, and coding.

Sarvam-105B is a top-tier performer, consistently matching or surpassing several major closed-source models and staying within a narrow margin of frontier models across diverse reasoning and agentic benchmarks. It demonstrates exceptional agentic and reasoning capabilities in real-world applications such as web search and technical troubleshooting.

A major focus during training was the Indian context and languages, resulting in state-of-the-art performance across 22 Indian languages for its model size.

Sarvam-105B is open-sourced under the Apache License. For more details, see our [blog](https://www.sarvam.ai/blogs/sarvam-30b-105b).

## #architecture

 Architecture

The 105B model adopts an MLA-style attention stack with decoupled QK head dimensions (q_head_dim=192 split into RoPE and noPE components, v_head_dim=128) and a large head_dim of 576, enabling higher representational bandwidth per head while keeping the hidden size at 4096. This approach improves attention expressivity and long-context extrapolation (via YaRN scaling with a factor of 40 and 128K context). It has an intermediate_size (16384) and moe_intermediate_size (2048), combined with top-8 routing over 128 experts, which increases per-token active capacity while keeping activation cost manageable. The model has one shared expert, a routed scaling factor of 2.5, and auxiliary-loss-free router balancing.

## #benchmarks

 Benchmarks

 Knowledge & Coding

Benchmark
Sarvam-105B
GLM-4.5-Air
GPT-OSS-120B
Qwen3-Next-80B-A3B-Thinking

Math500
98.6
97.2
97.0
98.2

Live Code Bench v6
71.7
59.5
72.3
68.7

MMLU
90.6
87.3
90.0
90.0

MMLU Pro
81.7
81.4
80.8
82.7

Writing Bench
80.5
83.8
86.5
84.6

Arena Hard v2
71.0
68.1
88.5
68.2

IF Eval
84.8
83.5
85.4
88.9

 Reasoning & Math

Benchmark
Sarvam-105B
GLM-4.5-Air
GPT-OSS-120B
Qwen3-Next-80B-A3B-Thinking

GPQA Diamond
78.7
75.0
80.1
77.2

AIME 25 (w/ Tools)
88.3 (96.7)
83.3
90.0
87.8

Beyond AIME
69.1
61.5
51.0
68.0

HMMT (Feb 25)
85.8
69.2
90.0
73.9

HMMT (Nov 25)
85.8
75.0
90.0
80.0

 Agentic

Benchmark
Sarvam-105B
GLM-4.5-Air
GPT-OSS-120B
Qwen3-Next-80B-A3B-Thinking

BrowseComp
49.5
21.3
-
38.0

SWE Bench Verified (SWE-Agent Harness)
45.0
57.6
50.6
60.9

τ² Bench (avg.)
68.3
53.2
65.8
55.0

See footnote for evaluation details.

## #inference

 Inference

 Huggingface

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

model_name = "sarvamai/sarvam-105b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto")

def generate_text(
 prompt: str,
 max_new_tokens: int = 2048,
 temperature: float = 0.8,
 top_p: float = 0.95,
 repetition_penalty: float = 1.0,
) -> None:
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")

 generation_config = GenerationConfig(
 max_new_tokens=max_new_tokens,
 repetition_penalty=repetition_penalty,
 temperature=temperature,
 top_p=top_p,
 do_sample=True,
 )

 with torch.no_grad():
 output_ids = model.generate(
 input_ids=inputs["input_ids"],
 attention_mask=inputs["attention_mask"],
 generation_config=generation_config,
 )
 return tokenizer.decode(output_ids[0], skip_special_tokens=True)

prompts = [
 "Which country won the FIFA World Cup in 2012?",
]

for prompt in prompts:
 templated_prompt = tokenizer.apply_chat_template(
 [{"role": "user", "content": prompt}],
 tokenize=False,
 add_generation_prompt=True,
 enable_thinking=True
 )
 output = generate_text(templated_prompt, max_new_tokens=512)
 print("Prompt: ", prompt)
 print("Generated text: ", output)
 print("=" * 100)

 SGLang

Install latest SGLang from source

git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install -e "python[all]"

Instantiate model and Run

import sglang as sgl
from transformers import AutoTokenizer

model_path = "sarvamai/sarvam-105b"
engine = sgl.Engine(
 model_path=model_path,
 tp_size=4,
 mem_fraction_static=0.70,
 trust_remote_code=True,
 dtype="bfloat16",
 moe_runner_backend="flashinfer_cutedsl",
 prefill_attention_backend="fa3",
 decode_attention_backend="flashmla",
 disable_radix_cache=False,
)

sampling_params = {
 "temperature": 0.8,
 "max_new_tokens": 2048,
 "repetition_penalty": 1.0,
}

prompts = [
 "Which band released the album Dark Side of the Moon in 1973?",
]

outputs = engine.generate([
 tokenizer.apply_chat_template([
 {"role": "user", "content": prompt}],
 tokenize=False,
 add_generation_prompt=True,
 enable_thinking=True)
 for prompt in prompts],
 sampling_params)
for p, o in zip(prompts, outputs):
 print("Prompt: ", p)
 print("Generated text: ", o['text'])
 print("=" * 100)

 vLLM

Note: currently a PR is open for native support for the Sarvam models in vLLM ([link](https://github.com/vllm-project/vllm/pull/33942)). Therefore, we have 2 options here.

#### #option-1-install-from-source-hard

 Option 1: install from source (hard)

- Use the custom fork here: [link](https://github.com/rahul-sarvam/vllm)

- Follow the instructions here to install from source: [link](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source)

#### #option-2-hot-patch-easy

 Option 2: hot-patch (easy)

- Run [hotpatch_vllm.py](/sarvamai/sarvam-105b/blob/main/./hotpatch_vllm.py)

- This will do the following:
install vllm=0.15.0

- add 2 model entries to registry.py

- download the model executors for sarvam-105b and sarvam-30b

Once this is done, you can run vLLM as usual

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_path = "sarvamai/sarvam-105b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
llm = LLM(model=model_path,
 trust_remote_code=True,
 max_model_len=2048,
 tensor_parallel_size=8,
 max_num_seqs=16,
 )
sampling_params = SamplingParams(
 temperature=0.8,
 max_tokens=2048,
 repetition_penalty=1.0,
 spaces_between_special_tokens=True
 )

prompts = [
 "Which artist painted The Persistence of Memory (the melting clocks)?",
]

outputs = llm.generate([
 tokenizer.apply_chat_template([
 {"role": "user", "content": prompt}],
 tokenize=False,
 add_generation_prompt=True,
 enable_thinking=True)
 for prompt in prompts],
 sampling_params)
for p, o in zip(prompts, outputs):
 print("Prompt: ", p)
 print("Generated text: ", o.outputs[0].text)
 print("=" * 100)

## #footnote

 Footnote

- General settings: All benchmarks are evaluated with a maximum context length of 65,536 tokens.

- Reasoning & Math benchmarks (Math500, MMLU, MMLU Pro, GPQA Diamond, AIME 25, Beyond AIME, HMMT): Evaluated with temperature=1.0, top_p=1.0, max_new_tokens=65536.

- Coding & Knowledge benchmarks (Live Code Bench v6, Arena Hard v2, IF Eval):
Evaluated with temperature=1.0, top_p=1.0, max_new_tokens=65536.

- Writing Bench:
Responses generated using official Writing-Bench parameters:
temperature=0.7, top_p=0.8, top_k=20, max_length=16000.
Scoring performed using the official Writing-Bench critic model with:
temperature=1.0, top_p=0.95, max_length=2048.

- Agentic benchmarks (BrowseComp, SWE Bench Verified, τ² Bench): Evaluated with temperature=0.5, top_p=1.0, max_new_tokens=32768.

## #citation

 Citation

@misc{sarvam_sovereign_models,
 title = {Introducing Sarvam's Sovereign Models},
 author = {{Sarvam Foundation Models Team}},
 year = {2026},
 howpublished = {\url{https://www.sarvam.ai/blogs/sarvam-30b-105b}},
 note = {Accessed: 2026-03-03}
}

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Good, I have enough data now to compile the tech radar scan. Let me now format the message according to the specified format and send it to 6558388607. I also need to update the memory file.

From the data I've gathered:

## GitHub Trending:
1. **666ghj/MiroFish** - Swarm intelligence engine (+345 today) - New entry
2. **openai/skills** - Codex Skills Catalog (+947 today) - OPENCLAW ECOSYSTEM, previously reported but still trending strong
3. **msitarzewski/agency-agents** - Complete AI agency agents - MARKETING RELEVANT
4. **GoogleCloudPlatform/generative-ai** - Gemini samples (+348 today)
5. **agentjido/jido** - Autonomous agent framework for Elixir (+138 today) - AGENT FRAMEWORK
6. **QwenLM/Qwen-Agent** - Agent framework with MCP support - OPENCLAW ECOSYSTEM
7. **alibaba/page-agent** - JavaScript GUI agent (+104 today)
8. **microsoft/hve-core** - Copilot components (+217 today)

## Hacker News highlights:
1. Argus - VSCode debugger for Claude Code (25 pts)
2. Ki Editor - AST-based editor (236 pts)
3. Plasma Bigscreen (561 pts)
4. UUID package in Go stdlib (290 pts)
5. Meta fair use BitTorrent argument (248 pts)
6. Claude Code re-ignited passion for 60yo dev (825 pts)
7. LLMs work best with acceptance criteria first (349 pts)
8. QGIS 4.0 (154 pts)
9. Helix editor (253 pts)
10. Galileo's notes found (185 pts)
11. Flash radiotherapy cancer treatment (26 pts)
12. Tinnitus connected to sleep (60 pts)

I notice significant research advances in transformer architectures and AI evaluation methods. The arXiv papers reveal deep insights into neural network mechanisms and potential improvements in LLM judgment systems. The HuggingFace trending models demonstrate rapid innovation in language model capabilities, with multiple variants of Qwen3.5 and specialized reasoning models emerging across different parameter sizes and training approaches.

The model landscape shows intense competition and refinement, with uncensored and distillation techniques pushing performance boundaries. Sarvam's 105B multilingual model and specialized reasoning models indicate sophisticated approaches to expanding language model versatility.

Agent frameworks like Jido in Elixir and SkillNet's comprehensive skill repository suggest growing ecosystem complexity. While not direct OpenClaw competitors, these developments signal a rapidly maturing AI agent infrastructure with increasingly sophisticated tooling and architectural approaches.

Now let me compile the tech radar scan and send it to Telegram:

message

📡 **Tech Radar** — 12:02 PM EST, March 7, 2026

🚀 **RISING REPOS:**
• **openai/skills** — Skills Catalog for Codex. Write once, use everywhere. ⭐ +947 today
  [Source](https://github.com/openai/skills)
  **[FLAG: OpenClaw ecosystem — agent skills infrastructure]**

• **666ghj/MiroFish** — Swarm intelligence engine predicting anything via multi-agent simulation ⭐ +345 today
  [Source](https://github.com/666ghj/MiroFish)

• **msitarzewski/agency-agents** — Complete AI agency at your fingertips - specialized agents with personality
  [Source](https://github.com/msitarzewski/agency-agents)
  **[FLAG: Marketing relevant — content generation workflows]**

• **agentjido/jido** — Autonomous agent framework for Elixir. Built for distributed, autonomous behavior ⭐ +138 today
  [Source](https://github.com/agentjido/jido)
  **[FLAG: Agent architecture — OTP runtime for multi-agent workflows]**

• **QwenLM/Qwen-Agent** — Agent framework with Function Calling, MCP, Code Interpreter, RAG
  [Source](https://github.com/QwenLM/Qwen-Agent)
  **[FLAG: OpenClaw ecosystem — native MCP support]**

📄 **FRESH RESEARCH:**
• **SkillNet: Create, Evaluate, and Connect AI Skills** — Open infrastructure for skill accumulation. 200K+ skills, 40% reward improvement.
  [HuggingFace](https://huggingface.co/papers/2603.04448)
  **[FLAG: OpenClaw ecosystem — skill transfer infrastructure]**

• **Bias-Bounded LLM Judges** — Framework for formally guaranteed bias reduction in LLM-as-a-Judge systems
  [arXiv](https://arxiv.org/abs/2603.05485)

• **Massive Activations and Attention Sinks** — Transformer architecture study showing pre-norm enables co-occurrence
  [arXiv](https://arxiv.org/abs/2603.05498)

🔥 **TRENDING MODELS:**
• **Sarvam-105B** — 106B MoE model optimized for Indian languages + reasoning. Top-tier agentic performance.
  [HuggingFace](https://huggingface.co/sarvamai/sarvam-105b)
  **[FLAG: SOTA claim — matches closed-source models on reasoning]**

• **Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled** — Claude reasoning distilled into Qwen
  [HuggingFace](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled)

⚡ **INFRA ALERTS:**
None detected this scan.

💡 **Hacker News Highlights:**
• [Claude Code re-ignited passion for 60yo dev](https://news.ycombinator.com/item?id=47282777) — 825 pts
• [LLMs work best with acceptance criteria first](https://news.ycombinator.com/item?id=47283337) — 349 pts
• [Argus: VSCode debugger for Claude Code](https://github.com/yessGlory17/argus) — 25 pts


Good! The message was sent successfully. Now I need to update the memory file with all the URLs I found, tagging them appropriately. Let me append to memory/2026-03-07.md.

edit

---

## Afternoon Briefing (17:00 UTC / 12:00 PM EST)

### URLs Reported:
1. https://github.com/yessGlory17/argus — Argus VSCode debugger for Claude Code
2. https://ki-editor.org/ — Ki Editor AST-based code editor
3. https://github.com/golang/go/issues/62026 — UUID package Go standard library
4. https://torrentfreak.com/uploading-pirated-books-via-bittorrent-qualifies-as-fair-use-meta/ — Meta fair use BitTorrent argument
5. https://github.com/openai/skills — OpenAI Skills Catalog for Codex
6. https://github.com/alibaba/page-agent — Alibaba JavaScript in-page GUI agent
7. https://apnews.com/article/canada-carney-andrew-succession-a7dc4996e0c091e956bcf0daf39c56ad — Canada PM Carney Prince Andrew succession
8. https://apnews.com/article/severe-weather-storm-tornado-midwest-oklahoma-f4666951e5ef045520e839923b788cba — Tornadoes Michigan Oklahoma 6 dead
9. https://www.bbc.com/news/articles/c4g0dzg6e4mo — Epstein files Trump accusations released
10. https://www.bbc.com/news/articles/cn87pl8e2l5o — Nairobi floods 23 dead Kenya
11. https://apnews.com/article/oil-gasoline-iran-war-inflation-1a1b7c3e5fbd735aa87c43ac664501cb — Oil gas prices Iran war
12. https://www.reuters.com — Private credit investors asking money back
13. https://www.bbc.co.uk/news/resources/idt-20f878f1-f4af-4022-9f62-b0515b9f4b20 — Iceberg A23a disintegration
14. https://spectrum.ieee.org/flash-radiotherapy — Millisecond cancer treatment
15. https://www.sciencealert.com/tinnitus-is-somehow-connected-to-a-crucial-bodily-function — Tinnitus sleep connection

### Diversity Check:
✅ Max 2 items per topic
✅ Mix of sources: GitHub (3), AP (3), BBC (3), Reuters (1), IEEE (1), ScienceAlert (1)
✅ Category spread: Tech (3), AI (3), Global (4), Markets (2), Science (3)
✅ All items <24h old

### Issues:
- web_search API 401 errors (Perplexity auth)
- SearXNG Docker unavailable (JSON parse error)
- Used direct web_fetch from HN, GitHub, Reuters, BBC, AP as fallback

---

## Tech Radar Hourly (17:02 UTC / 12:02 PM EST)

### URLs Reported:
1. https://github.com/openai/skills — Codex Skills Catalog (+947 today) #openclaw-ecosystem
2. https://github.com/666ghj/MiroFish — Swarm intelligence engine (+345 today)
3. https://github.com/msitarzewski/agency-agents — Complete AI agency agents #marketing-relevant
4. https://github.com/agentjido/jido — Elixir autonomous agent framework (+138 today)
5. https://github.com/QwenLM/Qwen-Agent — Agent framework with MCP support #openclaw-ecosystem
6. https://huggingface.co/papers/2603.04448 — SkillNet: Create, Evaluate, and Connect AI Skills #openclaw-ecosystem
7. https://arxiv.org/abs/2603.05485 — Bias-Bounded LLM Judges
8. https://arxiv.org/abs/2603.05498 — Massive Activations and Attention Sinks
9. https://huggingface.co/sarvamai/sarvam-105b — Sarvam-105B multilingual model #sota-model
10. https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled — Claude reasoning distilled
11. https://news.ycombinator.com/item?id=47282777 — Claude Code passion reignited (825 pts)
12. https://news.ycombinator.com/item?id=47283337 — LLMs with acceptance criteria first (349 pts)
13. https://github.com/yessGlory17/argus — Argus VSCode debugger for Claude Code

### Flags Raised:
- ⚠ openai/skills: +947 stars today (highest velocity today)
- ⚠ SkillNet paper: 200K+ skills repository, 40% reward improvement — relevant for agent skill transfer
- ⚠ Sarvam-105B: Claims to match closed-source models on reasoning benchmarks
- ⚠ Qwen-Agent: Native MCP support — potential OpenClaw integration
- ⚠ agency-agents: Marketing-relevant content automation


Successfully replaced text in /home/crogers2287/.openclaw/agents/magoo/memory/2026-03-07.md.

     ...
 147 ### Issues:
 148 - web_search API 401 errors (Perplexity auth)
 149 - SearXNG Docker unavailable (JSON parse error)
 150 - Used direct web_fetch from HN, GitHub, Reuters, BBC, AP as fallback
+151
+152 ---
+153
+154 ## Tech Radar Hourly (17:02 UTC / 12:02 PM EST)
+155
+156 ### URLs Reported:
+157 1. https://github.com/openai/skills — Codex Skills Catalog (+947 today) #openclaw-ecosystem
+158 2. https://github.com/666ghj/MiroFish — Swarm intelligence engine (+345 today)
+159 3. https://github.com/msitarzewski/agency-agents — Complete AI agency agents #marketing-relevant
+160 4. https://github.com/agentjido/jido — Elixir autonomous agent framework (+138 today)
+161 5. https://github.com/QwenLM/Qwen-Agent — Agent framework with MCP support #openclaw-ecosystem
+162 6. https://huggingface.co/papers/2603.04448 — SkillNet: Create, Evaluate, and Connect AI Skills #openclaw-ecosystem
+163 7. https://arxiv.org/abs/2603.05485 — Bias-Bounded LLM Judges
+164 8. https://arxiv.org/abs/2603.05498 — Massive Activations and Attention Sinks
+165 9. https://huggingface.co/sarvamai/sarvam-105b — Sarvam-105B multilingual model #sota-model
+166 10. https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled — Claude reasoning distilled
+167 11. https://news.ycombinator.com/item?id=47282777 — Claude Code passion reignited (825 pts)
+168 12. https://news.ycombinator.com/item?id=47283337 — LLMs with acceptance criteria first (349 pts)
+169 13. https://github.com/yessGlory17/argus — Argus VSCode debugger for Claude Code
+170
+171 ### Flags Raised:
+172 - ⚠ openai/skills: +947 stars today (highest velocity today)
+173 - ⚠ SkillNet paper: 200K+ skills repository, 40% reward improvement — relevant for agent skill transfer
+174 - ⚠ Sarvam-105B: Claims to match closed-source models on reasoning benchmarks
+175 - ⚠ Qwen-Agent: Native MCP support — potential OpenClaw integration
+176 - ⚠ agency-agents: Marketing-relevant content automation


Perfect! I've completed the tech radar scan. Let me now write a closing summary to the user explaining what was done.

Summary of what I did:
1. ✅ Read boot sequence files (USER.md, LEARNINGS.md, memory files, MEMORY.md)
2. ✅ Attempted SearXNG searches (failed due to Docker/service issues)
3. ✅ Used web_fetch to gather data from GitHub trending, Hacker News, arXiv, HuggingFace
4. ✅ Compiled tech radar report in specified format
5. ✅ Sent report to 6558388607 via message tool
6. ✅ Updated memory/2026-03-07.md with all URLs and flags

Key findings:
- **Highest velocity**: openai/skills (+947 stars today) — agent skills infrastructure for Codex
- **OpenClaw ecosystem**: Qwen-Agent (MCP support), SkillNet paper (skill transfer), openai/skills
- **Marketing relevant**: msitarzewski/agency-agents (specialized AI agents for content)
- **SOTA claim**: Sarvam-105B claims to match closed-source models on reasoning
- **Architecture**: agentjido/jido — Elixir autonomous agent framework

No immediate alerts (no repos with >100 stars/hour claiming to be OpenClaw competitors, no new MCP servers for major platforms, no SOTA claims on major benchmarks beyond what's normal).

Tech radar scan complete. Sent report to 6558388607 and logged to memory.

**What I found:**
- **Top rising**: openai/skills (+947 today) — Codex skills infrastructure, highly relevant for agent ecosystems
- **OpenClaw flags**: Qwen-Agent (native MCP), SkillNet paper (200K+ skills, 40% reward boost)
- **Marketing**: agency-agents repo with specialized content agents
- **SOTA**: Sarvam-105B (106B MoE) claims to match closed-source models on reasoning

**No critical alerts** — no direct OpenClaw competitors, no viral MCP servers for major platforms, no breakthrough SOTA claims on coding benchmarks.

**Issues**: SearXNG still down (Docker/JSON parse error). Used direct web_fetch from GitHub, HN, arXiv, HuggingFace as fallback.