headers = {        "Authorization": f"Bearer {HF_TOKEN}",        "content-type": "application/json",    }    payload = {        "inputs": f"{query}\n\nAnswer:",        "temperature": temperature,        "max_new_tokens": int(max_new_tokens),    }    response = requests.post(url, headers=headers, data=json.dumps(payload))    response.raise_for_status()    return json.loads(response.content.decode("utf-8"))prompt = """You must respond using JSON format, with a single action and single action input.You may 'ask_star_coder' for help on coding problems.{arg_schema}EXAMPLES----Human: "So what's all this about a GIL?"AI Assistant:{{  "action": "ask_star_coder",  "action_input": {{"query": "What is a GIL?", "temperature": 0.0, "max_new_tokens": 100}}"}}Observation: "The GIL is python's Global Interpreter Lock"Human: "Could you please write a calculator program in LISP?"AI Assistant:{{  "action": "ask_star_coder",  "action_input": {{"query": "Write a calculator program in LISP", "temperature": 0.0, "max_new_tokens": 250}}}}Observation: "(defun add (x y) (+ x y))\n(defun sub (x y) (- x y ))"Human: "What's the difference between an SVM and an LLM?"AI Assistant:{{  "action": "ask_star_coder",  "action_input": {{"query": "What's the difference between SGD and an SVM?", "temperature": 1.0, "max_new_tokens": 250}}}}Observation: "SGD stands for stochastic gradient descent, while an SVM is a Support Vector Machine."BEGIN! Answer the Human's question as best as you are able.------Human: 'What's the difference between an iterator and an iterable?'AI Assistant:""".format(    arg_schema=ask_star_coder.args)from transformers import pipelinefrom langchain.llms import HuggingFacePipelinehf_model = pipeline(    "text-generation", model="cerebras/Cerebras-GPT-590M", max_new_tokens=200)original_model = HuggingFacePipeline(pipeline=hf_model)generated = original_model.predict(prompt, stop=["Observation:", "Human:"])print(generated)    Setting `pad_token_id` to `eos_token_id`:50256 for
JSONFormer is a library that wraps local HuggingFace pipeline models for structured decoding of a subset of the JSON Schema.
JSONFormer is a library that wraps local HuggingFace pipeline models for structured decoding of a subset of the JSON Schema. ->: headers = {        "Authorization": f"Bearer {HF_TOKEN}",        "content-type": "application/json",    }    payload = {        "inputs": f"{query}\n\nAnswer:",        "temperature": temperature,        "max_new_tokens": int(max_new_tokens),    }    response = requests.post(url, headers=headers, data=json.dumps(payload))    response.raise_for_status()    return json.loads(response.content.decode("utf-8"))prompt = """You must respond using JSON format, with a single action and single action input.You may 'ask_star_coder' for help on coding problems.{arg_schema}EXAMPLES----Human: "So what's all this about a GIL?"AI Assistant:{{  "action": "ask_star_coder",  "action_input": {{"query": "What is a GIL?", "temperature": 0.0, "max_new_tokens": 100}}"}}Observation: "The GIL is python's Global Interpreter Lock"Human: "Could you please write a calculator program in LISP?"AI Assistant:{{  "action": "ask_star_coder",  "action_input": {{"query": "Write a calculator program in LISP", "temperature": 0.0, "max_new_tokens": 250}}}}Observation: "(defun add (x y) (+ x y))\n(defun sub (x y) (- x y ))"Human: "What's the difference between an SVM and an LLM?"AI Assistant:{{  "action": "ask_star_coder",  "action_input": {{"query": "What's the difference between SGD and an SVM?", "temperature": 1.0, "max_new_tokens": 250}}}}Observation: "SGD stands for stochastic gradient descent, while an SVM is a Support Vector Machine."BEGIN! Answer the Human's question as best as you are able.------Human: 'What's the difference between an iterator and an iterable?'AI Assistant:""".format(    arg_schema=ask_star_coder.args)from transformers import pipelinefrom langchain.llms import HuggingFacePipelinehf_model = pipeline(    "text-generation", model="cerebras/Cerebras-GPT-590M", max_new_tokens=200)original_model = HuggingFacePipeline(pipeline=hf_model)generated = original_model.predict(prompt, stop=["Observation:", "Human:"])print(generated)    Setting `pad_token_id` to `eos_token_id`:50256 for