GLM4.7-Distill-LFM2.5-1.2B/README.md

---
base_model: LiquidAI/LFM2.5-1.2B-Instruct

model_type: causal-lm
architecture: LFM2

tags:
  - text-generation
  - text-generation-inference
  - instruction-tuned
  - distilled
  - synthetic-data
  - transformers
  - unsloth
  - lfm2
  - glm
  - agentic
  - edge
  - efficient

license: apache-2.0

language:
  - en

datasets:
  - Open-Orca/FLAN
  - databricks/databricks-dolly-15k
  - OpenAssistant/oasst1
  - BAAI/Infinity-Instruct
  - sahil2801/CodeAlpaca-20k
  - TIGER-Lab/MathInstruct

pipeline_tag: text-generation


---

# GLM4.7-Distill-LFM2.5-1.2B

<img src="logo_model.png" width="100%" />

## Model Overview

**GLM4.7-Distill-LFM2.5-1.2B** is a 1.2B-parameter instruction-following language model obtained via **offline distillation** from **GLM-4.7** into the **Liquid AI LFM2** architecture.

The model is designed to be:

* concise and non-verbose
* strong at instruction following
* efficient for local and edge deployments
* suitable for assistant, agentic, and system-integration use cases

This model does **not** include chain-of-thought reasoning and is optimized for **final-answer quality** rather than verbose explanations.

---

## Key Characteristics

* **Base architecture**: Liquid AI LFM2
* **Model size**: 1.2B parameters
* **Training method**: Offline supervised distillation (SFT with LoRA)
* **Teacher model**: GLM-4.7 (used only for data generation)
* **Inference dependency on teacher**: None
* **Reasoning traces**: Not included
* **Target behavior**: Clear, grounded, instruction-aligned responses

---

## Training Details

### Distillation Approach

This model was trained using **offline distillation**, where instruction-response pairs generated by **GLM-4.7** were combined with high-quality public instruction datasets.

The teacher model was **not used during training or inference**, and no teacher weights or logits are included.

Training focused on:

* instruction adherence
* response clarity
* reduced verbosity
* stable decision boundaries

### Datasets Used (Approx. 13K Samples)

The following datasets were sampled and combined:

* Open-Orca / FLAN
* Databricks Dolly 15K
* OpenAssistant OASST1
* BAAI Infinity-Instruct
* CodeAlpaca
* TIGER-Lab MathInstruct

These were augmented with **GLM-4.7–generated instruction responses**, with explicit avoidance of chain-of-thought reasoning.

---

## Intended Use

This model is well suited for:

* general-purpose assistants
* planning and task decomposition
* summarization and explanation
* lightweight coding assistance
* agentic workflows
* system integration and automation
* on-device or edge inference scenarios

---

## Limitations

Like other compact distilled models, this model may:

* hallucinate when given insufficient or false premises
* struggle with adversarial logical inference (NLI-style tasks)
* lack temporal awareness of recent events
* provide confident answers where explicit uncertainty is required

For critical reasoning, verification layers or external tools are recommended.

---

## Ethical & Responsible Use

* This model was trained on a mixture of public datasets and synthetic data.
* It does not contain personal data by design.
* Outputs should not be treated as authoritative in medical, legal, or safety-critical contexts.

---

## Citation & Acknowledgements

If you use this model in research or applications, please acknowledge:

* **GLM-4.7** for teacher-generated distillation data
* **Liquid AI** for the LFM2 architecture
* The creators of the public instruction datasets listed above

---

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "yasserrmd/GLM4.7-Distill-LFM2.5-1.2B"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
    # attn_implementation="flash_attention_2"  # uncomment on compatible GPU
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "Implement QuickSort algorithm with complexity analysis"

input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
).to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    top_p=0.1,
    repetition_penalty=1.05,
    max_new_tokens=512,
    streamer=streamer,
)
```

## With vLLM (Production)

```python
from vllm import LLM, SamplingParams

llm = LLM(model="yasserrmd/GLM4.7-Distill-LFM2.5-1.2B")

sampling_params = SamplingParams(
    temperature=0.1,
    top_k=50,
    top_p=0.1,
    repetition_penalty=1.05,
    max_tokens=512
)

prompts = [
    "Implement QuickSort algorithm",
    "Solve the Longest Common Subsequence problem",
    "Design a hash table with collision handling"
]

outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    print(output.outputs[0].text)
```

## Recommended Use

- Technical interviews
- Algorithm learning
- Code generation
- Problem-solving
- Code refactoring
- Educational tutoring

## Not Recommended For

- Current events or recent information
- Factual knowledge queries
- Legal, medical, or safety-critical code
- Highly specialized domain problems
- Real-time critical systems without human review

## License

Please refer to the licenses of:

* the base LFM2 model
* the individual datasets used for training

This repository follows the same usage constraints as the upstream components.