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Model: sarvamai/sarvam-m
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LICENSE.txt

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README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+- bn
+- hi
+- kn
+- gu
+- mr
+- ml
+- or
+- pa
+- ta
+- te
+base_model:
+- mistralai/Mistral-Small-3.1-24B-Base-2503
+base_model_relation: finetune
+---
+
+# Sarvam-M
+<p align="center">
+  <a href="https://dashboard.sarvam.ai/playground"
+     target="_blank" rel="noopener noreferrer">
+    <img
+      src="https://img.shields.io/badge/🚀 Chat on Sarvam&nbsp;Playground-1488CC?style=for-the-badge&logo=rocket"
+      alt="Chat on Sarvam Playground"
+    />
+  </a>
+</p>
+
+
+# Model Information
+
+`sarvam-m` is a multilingual, hybrid-reasoning, text-only language model built on Mistral-Small. This post-trained version delivers exceptional improvements over the base model:
+
+- +20% average improvement on Indian language benchmarks
+- +21.6% enhancement on math benchmarks
+- +17.6% boost on programming benchmarks
+
+Performance gains are even more impressive at the intersection of Indian languages and mathematics, with an outstanding +86% improvement in romanized Indian language GSM-8K benchmarks.
+
+Learn more about sarvam-m in our detailed [blog post](https://www.sarvam.ai/blogs/sarvam-m).
+
+# Key Features
+
+- **Hybrid Thinking Mode**: A single versatile model supporting both "think" and "non-think" modes. Use the think mode for complex logical reasoning, mathematical problems, and coding tasks, or switch to non-think mode for efficient, general-purpose conversation.
+
+- **Advanced Indic Skills**: Specifically post-trained on Indian languages alongside English, embodying a character that authentically reflects and emphasizes Indian cultural values.
+
+- **Superior Reasoning Capabilities**: Outperforms most similarly-sized models on coding and math benchmarks, demonstrating exceptional reasoning abilities.
+
+- **Seamless Chatting Experience**: Full support for both Indic scripts and romanized versions of Indian languages, providing a smooth and accessible multilingual conversation experience.
+
+# Quickstart 
+
+The following code snippet demonstrates how to use `sarvam-m` using Transformers. 
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "sarvamai/sarvam-m"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, torch_dtype="auto", device_map="auto"
+)
+
+# prepare the model input
+prompt = "Who are you and what is your purpose on this planet?"
+
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    enable_thinking=True,  # Switches between thinking and non-thinking modes. Default is True.
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(**model_inputs, max_new_tokens=8192)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()
+output_text = tokenizer.decode(output_ids)
+
+if "</think>" in output_text:
+    reasoning_content = output_text.split("</think>")[0].rstrip("\n")
+    content = output_text.split("</think>")[-1].lstrip("\n").rstrip("</s>")
+else:
+    reasoning_content = ""
+    content = output_text.rstrip("</s>")
+
+print("reasoning content:", reasoning_content)
+print("content:", content)
+```
+
+> [!NOTE]
+> For thinking mode, we recommend `temperature=0.5`; for no-think mode, `temperature=0.2`.
+
+
+# With Sarvam APIs
+
+```python
+from openai import OpenAI
+
+base_url = "https://api.sarvam.ai/v1"
+model_name = "sarvam-m"
+api_key = "Your-API-Key"  # get it from https://dashboard.sarvam.ai/
+
+client = OpenAI(
+    base_url=base_url,
+    api_key=api_key,
+).with_options(max_retries=1)
+
+messages = [
+    {"role": "system", "content": "You're a helpful AI assistant"},
+    {"role": "user", "content": "Explain quantum computing in simple terms"},
+]
+
+response1 = client.chat.completions.create(
+    model=model_name,
+    messages=messages,
+    reasoning_effort="medium",  # Enable thinking mode. `None` for disable.
+    max_completion_tokens=4096,
+)
+print("First response:", response1.choices[0].message.content)
+
+# Building messages for the second turn (using previous response as context)
+messages.extend(
+    [
+        {
+            "role": "assistant",
+            "content": response1.choices[0].message.content,
+        },
+        {"role": "user", "content": "Can you give an analogy for superposition?"},
+    ]
+)
+
+response2 = client.chat.completions.create(
+    model=model_name,
+    messages=messages,
+    reasoning_effort="medium",
+    max_completion_tokens=8192,
+)
+print("Follow-up response:", response2.choices[0].message.content)
+```
+
+Refer to API docs here: [sarvam Chat Completions API docs](https://docs.sarvam.ai/api-reference-docs/chat/completions)
+
+`reasoning_effort` can take three possible values: `low`, `medium`, and `high` to be consistent with the OpenAI API spec. Setting any of the three values just enables the thinking mode of sarvam-m.
+
+# VLLM Deployment
+
+For easy deployment, we can use `vllm>=0.8.5` and create an OpenAI-compatible API endpoint with `vllm serve sarvamai/sarvam-m`.
+
+If you want to use vLLM with python, you can do the following.
+
+```python
+from openai import OpenAI
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+models = client.models.list()
+model = models.data[0].id
+
+messages = [{"role": "user", "content": "Why is 42 the best number?"}]
+
+# By default, thinking mode is enabled.
+# If you want to disable thinking, add:
+# extra_body={"chat_template_kwargs": {"enable_thinking": False}}
+response = client.chat.completions.create(model=model, messages=messages)
+output_text = response.choices[0].message.content
+
+if "</think>" in output_text:
+    reasoning_content = output_text.split("</think>")[0].rstrip("\n")
+    content = output_text.split("</think>")[-1].lstrip("\n")
+else:
+    reasoning_content = ""
+    content = output_text
+
+print("reasoning content:", reasoning_content)
+print("content:", content)
+
+# For the next round, add the model's response directly as assistant turn.
+messages.append(
+    {"role": "assistant", "content": output_text}
+)
+```

config.json

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+{
+  "activation": "silu",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 32768,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": false,
+  "vocab_size": 131072
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.51.3",
+  "temperature": 0.4,
+  "use_cache": false
+}

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