Instructions to use chinmaykumar-vyas/sarvam-105b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chinmaykumar-vyas/sarvam-105b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="chinmaykumar-vyas/sarvam-105b", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("chinmaykumar-vyas/sarvam-105b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use chinmaykumar-vyas/sarvam-105b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chinmaykumar-vyas/sarvam-105b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chinmaykumar-vyas/sarvam-105b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/chinmaykumar-vyas/sarvam-105b
- SGLang
How to use chinmaykumar-vyas/sarvam-105b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "chinmaykumar-vyas/sarvam-105b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chinmaykumar-vyas/sarvam-105b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "chinmaykumar-vyas/sarvam-105b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chinmaykumar-vyas/sarvam-105b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use chinmaykumar-vyas/sarvam-105b with Docker Model Runner:
docker model run hf.co/chinmaykumar-vyas/sarvam-105b
File size: 3,143 Bytes
b29a6e1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | {{- '[@BOS@]\n' }}
{%- if tools -%}
<|start_of_turn|><|tool_declare|>
<tools>
{% for tool in tools %}
{{ tool | tojson(ensure_ascii=False) }}
{% endfor %}
</tools>
{{- '<|end_of_turn|>\n' }}{%- endif -%}
{%- macro visible_text(content) -%}
{%- if content is string -%}
{{- content }}
{%- elif content is iterable and content is not mapping -%}
{%- for item in content -%}
{%- if item is mapping and item.type == 'text' -%}
{{- item.text }}
{%- elif item is string -%}
{{- item }}
{%- endif -%}
{%- endfor -%}
{%- elif content is none -%}
{{- '' }}
{%- else -%}
{{- content }}
{%- endif -%}
{%- endmacro -%}
{%- set ns = namespace(last_user_index=-1) %}
{%- for m in messages %}
{%- if m.role == 'user' %}
{% set ns.last_user_index = loop.index0 -%}
{%- endif %}
{%- endfor %}
{% for m in messages %}
{%- if m.role == 'user' -%}<|start_of_turn|><|user|>
{{ visible_text(m.content) }}
{{- '<|nothink|>' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("<|nothink|>")) else '' -}}
{{- '<|end_of_turn|>\n' }}
{%- elif m.role == 'assistant' -%}
{{- '<|start_of_turn|><|assistant|>\n' }}
{%- set reasoning_content = '' %}
{%- set content = visible_text(m.content) %}
{%- if m.reasoning_content is string %}
{%- set reasoning_content = m.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_user_index and reasoning_content -%}
{{ '<think>' + reasoning_content.strip() + '</think>'}}
{%- else -%}
{{ '<think></think>' }}
{%- endif -%}
{%- if content.strip() -%}
{{ '\n' + content.strip() }}
{%- endif -%}
{% if m.tool_calls %}
{% for tc in m.tool_calls %}
{%- if tc.function %}
{%- set tc = tc.function %}
{%- endif %}
{{ '\n<tool_call>' + tc.name }}
{% set _args = tc.arguments %}
{% for k, v in _args.items() %}
<arg_key>{{ k }}</arg_key>
<arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>
{% endfor %}
</tool_call>{% endfor %}
{% endif %}
{{- '<|end_of_turn|>\n' }}
{%- elif m.role == 'tool' -%}
{%- if m.content is string -%}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|start_of_turn|><|observation|>' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- m.content }}
{{- '\n</tool_response>' }}
{%- else -%}
<|start_of_turn|><|observation|>{% for tr in m.content %}
<tool_response>
{{ tr.output if tr.output is defined else tr }}
</tool_response>{% endfor -%}
{% endif -%}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|end_of_turn|>\n' }}{%- endif -%}
{%- elif m.role == 'system' -%}
<|start_of_turn|><|system|>
{{ visible_text(m.content) }}
{{- '<|end_of_turn|>\n' }}
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{- '<|start_of_turn|><|assistant|>\n' }}
{%- endif -%} |