commit ae54dd31148ecc66f6a3f1b84ca7f87d3150add9 Author: ModelHub XC Date: Sun Jun 14 15:52:12 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: sarvamai/sarvam-m Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..4947287 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,177 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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+ + Chat on Sarvam Playground + +

+ + +# 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 "" in output_text: + reasoning_content = output_text.split("")[0].rstrip("\n") + content = output_text.split("")[-1].lstrip("\n").rstrip("") +else: + reasoning_content = "" + content = output_text.rstrip("") + +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 "" in output_text: + reasoning_content = output_text.split("")[0].rstrip("\n") + content = output_text.split("")[-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} +) +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..b6d5564 --- /dev/null +++ b/config.json @@ -0,0 +1,27 @@ +{ + "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, + 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+ "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "bos_token": "", + "chat_template": "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_think_system_message = \"You are a helpful assistant. 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