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fixing hf examples
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@ -94,34 +94,6 @@ print(response)
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You need to tell LiteLLM when you're calling Huggingface.
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This is done by adding the "huggingface/" prefix to `model`, example `completion(model="huggingface/<model_name>",...)`.
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<Tabs>
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<TabItem value="tgi" label="Text-generation-interface (TGI)">
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```python
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import os
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from litellm import completion
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# [OPTIONAL] set env var
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os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
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messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}]
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# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
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response = completion(
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model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
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messages=messages,
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api_base="https://my-endpoint.huggingface.cloud",
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stream=True
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)
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print(response)
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for chunk in response:
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print(chunk)
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```
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</TabItem>
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<TabItem value="conv" label="Conversational-task (BlenderBot, etc.)">
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```python
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import os
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from litellm import completion
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@ -143,32 +115,6 @@ print(response)
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for chunk in response:
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print(chunk)
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```
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</TabItem>
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<TabItem value="none" label="Non TGI/Conversational-task LLMs">
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```python
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import os
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from litellm import completion
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# [OPTIONAL] set env var
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os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
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messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}]
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# e.g. Call 'roneneldan/TinyStories-3M' hosted on HF Inference endpoints
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response = completion(
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model="huggingface/roneneldan/TinyStories-3M",
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messages=messages,
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api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud",
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stream=True
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)
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print(response)
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for chunk in response:
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print(chunk)
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```
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</TabItem>
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</Tabs>
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### [OPTIONAL] API KEYS + API BASE
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If required, you can set the api key + api base, set it in your os environment. [Code for how it's sent](https://github.com/BerriAI/litellm/blob/0100ab2382a0e720c7978fbf662cc6e6920e7e03/litellm/llms/huggingface_restapi.py#L25)
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