Text Generation
Transformers
Safetensors
falcon_h1
text-editing
rewriting
paraphrasing
instruct
conversational
Instructions to use appvoid/palmer-005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/palmer-005 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/palmer-005") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-005") model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-005", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/palmer-005 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/palmer-005" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/palmer-005", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/appvoid/palmer-005
- SGLang
How to use appvoid/palmer-005 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 "appvoid/palmer-005" \ --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": "appvoid/palmer-005", "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 "appvoid/palmer-005" \ --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": "appvoid/palmer-005", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use appvoid/palmer-005 with Docker Model Runner:
docker model run hf.co/appvoid/palmer-005
File size: 2,707 Bytes
a8549df | 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 | {# --- System Prompt Handling --- #}
{%- if messages and messages[0]['role'] == 'system' %}
{%- set remaining_messages = messages[1:] %}
{%- else %}
{%- set remaining_messages = messages %}
{%- endif %}
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n' }}
{%- endif %}
# Tools
You may call one or more functions to assist with the user query. You are provided with function signatures within <tools></tools> XML tags.
<tools>
{%- for tool in tools %}
{{- "" }}
{{ tool | tojson }}
{%- endfor %}
{{- "" }}
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- '<|im_end|>\n' }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{# --- Render remaining messages --- #}
{%- for message in remaining_messages %}
{%- if message['role'] == 'user' %}
{{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>\n' }}
{%- elif message['role'] == 'assistant' %}
{{- '<|im_start|>' + message['role'] +'\n' }}
{%- if message.get('content','') %}
{{- message['content'] + '\n' }}
{%- endif %}
{%- if tools and message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{-'<tool_call>\n' }}
{{- '{"name": "'+ tool_call.name + '", "arguments":' }}
{%- if tool_call.arguments is string -%}
{{ tool_call.arguments }}
{%- else -%}
{{ tool_call.arguments | tojson }}
{%- endif -%}
{{- '}' }}
{{- '\n</tool_call>\n' }}
{%- endfor %}
{%- endif %}
{%- if not loop.last %}
{{- '<|im_end|>' + '\n' }}
{%- else %}
{{- '<|im_end|>' }}
{%- endif %}
{%- elif message['role'] == 'tool' %}
{# Tool responses treated as user messages #}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' + message['content'] + '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{# --- Add generation prompt after last message if requested --- #}
{%- if loop.last and add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}
{%- endfor %}
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