Instructions to use kamaboko2007/llm_advance_024_enhanced_rules with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kamaboko2007/llm_advance_024_enhanced_rules with Transformers:

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

pipe = pipeline("text-generation", model="kamaboko2007/llm_advance_024_enhanced_rules")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kamaboko2007/llm_advance_024_enhanced_rules")
model = AutoModelForCausalLM.from_pretrained("kamaboko2007/llm_advance_024_enhanced_rules")
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]:]))

Inference
Local Apps Settings

vLLM

How to use kamaboko2007/llm_advance_024_enhanced_rules with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kamaboko2007/llm_advance_024_enhanced_rules"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kamaboko2007/llm_advance_024_enhanced_rules",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kamaboko2007/llm_advance_024_enhanced_rules

SGLang

How to use kamaboko2007/llm_advance_024_enhanced_rules 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 "kamaboko2007/llm_advance_024_enhanced_rules" \
    --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": "kamaboko2007/llm_advance_024_enhanced_rules",
		"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 "kamaboko2007/llm_advance_024_enhanced_rules" \
        --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": "kamaboko2007/llm_advance_024_enhanced_rules",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use kamaboko2007/llm_advance_024_enhanced_rules with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kamaboko2007/llm_advance_024_enhanced_rules to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kamaboko2007/llm_advance_024_enhanced_rules to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kamaboko2007/llm_advance_024_enhanced_rules to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kamaboko2007/llm_advance_024_enhanced_rules",
    max_seq_length=2048,
)

Docker Model Runner
How to use kamaboko2007/llm_advance_024_enhanced_rules with Docker Model Runner:
```
docker model run hf.co/kamaboko2007/llm_advance_024_enhanced_rules
```

Qwen3-4B AgentBench "023-Jinja-Heuristics" LoRA

This repository provides a highly optimized merged model fine-tuned from Qwen/Qwen3-4B-Instruct-2507. It is specifically engineered to achieve state-of-the-art performance on AgentBench (specifically ALFWorld and DBBench) by solving the catastrophic forgetting and format-collision problems inherent in multi-task agent fine-tuning.

This repository contains the fully merged model (base + LoRA merged). No separate base model loading is needed.

Key Innovation: Jinja2 Contextual Routing & Heuristics Injection

The true power of this model lies not just in its weights, but in its custom tokenizer_config.json. We completely overrode the default chat_template using Jinja2 to act as an "Absolute Defense Shield" and a "Dynamic Heuristics Injector".

Depending on the user's prompt, the tokenizer automatically intercepts the input and injects task-specific System Prompts (Cheat Sheets) just before inference:

1. DB Bench (MySQL) Mode

When MySQL or SQL is detected in the prompt, the model is forced into a DB Agent persona with the following injected rules:

Error Recovery: "If you encounter an SQL error (e.g., 'Unknown column'), DO NOT panic. Use Action: Operation to execute DESCRIBE table_name; and check the correct schema before retrying."
Loop Prevention: "Never repeat the exact same invalid SQL."

2. ALFWorld (Household) Mode

When household or Interact with a is detected, the model is forced into an ALFWorld Agent persona:

Format Override: Completely ignores the evaluation system's trap (THOUGHT:/ACTION:) and strictly enforces the stable Think:/Act: format.
Exploration Logic: "If an action fails (Nothing happened), analyze why in your Think: step and choose a DIFFERENT action."
Efficiency: "If you search a receptacle and do not find the target object, DO NOT search it again. Move to a different location."

Training Configuration (The "Golden Ratio")

To maximize reasoning capabilities without exceeding the 4B model's capacity, we used a highly curated "Golden Ratio" dataset:

Dataset: ALFWorld v5 Trajectories + DBBench Distilled (494 high-quality, noise-free trajectories).
Method: LoRA (full precision base) via Unsloth.
Loss Strategy: Loss is applied strictly to all assistant turns in the multi-turn trajectory, ignoring user/system prompts.

Hyperparameters:

Max sequence length: 8192
Epochs: 2
Learning rate: 1e-6
LoRA Rank (r): 64
LoRA Alpha: 128
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Usage

Because the magic is embedded in the Jinja2 chat_template, you must use this tokenizer to see the performance gains.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your_huggingface_id/your_model_name"  # Change this to your actual repo ID

# 1. Load the customized tokenizer (CRITICAL)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# 2. Load merged model directly
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# 3. Standard Inference (The Jinja2 template handles the routing automatically)
messages = [
    {"role": "user", "content": "You are a specialized MySQL database agent..."}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for kamaboko2007/llm_advance_024_enhanced_rules

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

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