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85dcfbef20
* Add model * Update loader * Fix llama config usage * Docs * Fix config no_rope_layers * Fix tie_word_embeddings default * Add chat template * Embed the chat templates * Fix embedding template * enable_thinking default true * Update examples * XML tools for smollm3 * Add smollm3 docs * Fix openai examples * Clippy --------- Co-authored-by: Eric Buehler <[email protected]>
50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
from mistralrs import Runner, Which, ChatCompletionRequest, Architecture
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from json import dumps
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runner = Runner(
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which=Which.Plain(
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model_id="microsoft/Phi-3.5-mini-instruct",
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),
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num_device_layers=["500"],
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)
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# In fact, JSON object can be also defined in the grammar itself, see
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# lark_llg.py and https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#inline-json-schemas
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# @myobj will reference the JSON schema defined below (see grammars = [ ... ])
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top_lark = r"""
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start: "Reasoning: " /.+/ "\nJSON: " @myobj
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"""
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answer_schema = {
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"type": "object",
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"properties": {
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"answer": {"type": "string", "enum": ["Yes", "No"]},
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},
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"required": ["answer"],
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"additionalProperties": False,
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}
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grammars = [
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{"lark_grammar": top_lark},
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{"name": "myobj", "json_schema": answer_schema},
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]
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res = runner.send_chat_completion_request(
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ChatCompletionRequest(
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model="default",
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messages=[
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{
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"role": "user",
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"content": "If all dogs are mammals, and all mammals are animals, are dogs animals?",
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}
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],
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max_tokens=30,
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temperature=0.1,
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grammar_type="llguidance",
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grammar=dumps({"grammars": grammars}),
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)
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)
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print(res.choices[0].message.content)
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print(res.usage)
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