Instructions to use Ba2han/model-translator-lfm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ba2han/model-translator-lfm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Ba2han/model-translator-lfm", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ba2han/model-translator-lfm", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Ba2han/model-translator-lfm", trust_remote_code=True, 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 Ba2han/model-translator-lfm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ba2han/model-translator-lfm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ba2han/model-translator-lfm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ba2han/model-translator-lfm

SGLang

How to use Ba2han/model-translator-lfm 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 "Ba2han/model-translator-lfm" \
    --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": "Ba2han/model-translator-lfm",
		"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 "Ba2han/model-translator-lfm" \
        --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": "Ba2han/model-translator-lfm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Ba2han/model-translator-lfm with Docker Model Runner:
```
docker model run hf.co/Ba2han/model-translator-lfm
```

Ba2han commited on Jan 4

Commit

c653a37

verified ·

1 Parent(s): 58192fc

Update tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +4 -3

tokenizer_config.json CHANGED Viewed

@@ -4059,7 +4059,7 @@
       "special": true
     }
   },
-  "bos_token": null,
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "extra_special_tokens": {},
@@ -4076,5 +4076,6 @@
   "tokenizer_class": "PreTrainedTokenizerFast",
   "unk_token": null,
   "use_default_system_prompt": false,
-  "use_fast": true
-}

       "special": true
     }
   },
+  "bos_token": "<|startoftext|>",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "extra_special_tokens": {},
   "tokenizer_class": "PreTrainedTokenizerFast",
   "unk_token": null,
   "use_default_system_prompt": false,
+  "use_fast": true,
+  "chat_template": "{{- bos_token -}}{%- set system_prompt = \"\" -%}{%- set ns = namespace(system_prompt=\"\") -%}{%- if messages[0][\"role\"] == \"system\" -%} {%- set ns.system_prompt = messages[0][\"content\"] -%} {%- set messages = messages[1:] -%}{%- endif -%}{%- if tools -%} {%- set ns.system_prompt = ns.system_prompt + (\"\n\" if ns.system_prompt else \"\") + \"List of tools: <|tool_list_start|>[\" -%} {%- for tool in tools -%} {%- if tool is not string -%} {%- set tool = tool | tojson -%} {%- endif -%} {%- set ns.system_prompt = ns.system_prompt + tool -%} {%- if not loop.last -%} {%- set ns.system_prompt = ns.system_prompt + \", \" -%} {%- endif -%} {%- endfor -%} {%- set ns.system_prompt = ns.system_prompt + \"]<|tool_list_end|>\" -%}{%- endif -%}{%- if ns.system_prompt -%} {{- \"<|im_start|>system\n\" + ns.system_prompt + \"<|im_end|>\n\" -}}{%- endif -%}{%- for message in messages -%} {{- \"<|im_start|>\" + message[\"role\"] + \"\n\" -}} {%- set content = message[\"content\"] -%} {%- if content is not string -%} {%- set content = content | tojson -%} {%- endif -%} {%- if message[\"role\"] == \"tool\" -%} {%- set content = \"<|tool_response_start|>\" + content + \"<|tool_response_end|>\" -%} {%- endif -%} {{- content + \"<|im_end|>\n\" -}}{%- endfor -%}{%- if add_generation_prompt -%} {{- \"<|im_start|>assistant\n\" -}}{%- endif -%}"
+}