Text Generation
Transformers
Safetensors
PEFT
llama
merged
adapter
conversational
text-generation-inference
Instructions to use mekala-2402/final_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mekala-2402/final_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mekala-2402/final_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mekala-2402/final_model") model = AutoModelForCausalLM.from_pretrained("mekala-2402/final_model") 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]:])) - PEFT
How to use mekala-2402/final_model with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mekala-2402/final_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mekala-2402/final_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mekala-2402/final_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mekala-2402/final_model
- SGLang
How to use mekala-2402/final_model 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 "mekala-2402/final_model" \ --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": "mekala-2402/final_model", "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 "mekala-2402/final_model" \ --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": "mekala-2402/final_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mekala-2402/final_model with Docker Model Runner:
docker model run hf.co/mekala-2402/final_model
| FROM /content/mekala-2402/model_2/unsloth.F16.gguf | |
| TEMPLATE """{{ if .Messages }} | |
| {{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|> | |
| {{- if .System }} | |
| {{ .System }} | |
| {{- end }} | |
| {{- if .Tools }} | |
| You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the original use question. | |
| {{- end }} | |
| {{- end }}<|eot_id|> | |
| {{- range $i, $_ := .Messages }} | |
| {{- $last := eq (len (slice $.Messages $i)) 1 }} | |
| {{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|> | |
| {{- if and $.Tools $last }} | |
| Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. | |
| Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables. | |
| {{ $.Tools }} | |
| {{- end }} | |
| {{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|> | |
| {{ end }} | |
| {{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|> | |
| {{- if .ToolCalls }} | |
| {{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }} | |
| {{- else }} | |
| {{ .Content }}{{ if not $last }}<|eot_id|>{{ end }} | |
| {{- end }} | |
| {{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|> | |
| {{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|> | |
| {{ end }} | |
| {{- end }} | |
| {{- end }} | |
| {{- else }} | |
| {{- if .System }}<|start_header_id|>system<|end_header_id|> | |
| {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|> | |
| {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|> | |
| {{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}""" | |
| PARAMETER stop "<|start_header_id|>" | |
| PARAMETER stop "<|end_header_id|>" | |
| PARAMETER stop "<|eot_id|>" | |
| PARAMETER stop "<|eom_id|>" | |
| PARAMETER temperature 1.5 | |
| PARAMETER min_p 0.1 |