Instructions to use GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct", 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 GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct

SGLang

How to use GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct 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 "GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct" \
    --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": "GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct",
		"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 "GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct" \
        --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": "GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct with Docker Model Runner:
```
docker model run hf.co/GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct

SFT-версия модели LinguaLaboratoriumMechanicus-Qwen-1.7B на Q&A по лору WH40k.

Исходная база: Qwen/Qwen3-1.7B-Base (Apache-2.0).

Использование

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype=torch.bfloat16,
).to(device)

messages = [{"role": "user", "content": "Что такое Гибельный шторм?"}]
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_tensors="pt",
    enable_thinking=False,
).to(device)

output_ids = model.generate(
    input_ids,
    max_new_tokens=120,
    do_sample=False,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=True))

Связанные репозитории

Qwen/Qwen3-1.7B-Base - исходная база
LinguaLaboratoriumMechanicus-Qwen-1.7B - CPT

Citation

https://huggingface.co/GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct

Downloads last month: 14

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B-instruct

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

GoldenGekko/LinguaLaboratoriumMechanicus-Qwen-1.7B

Finetuned

(1)

this model

Quantizations

1 model