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Model: yasserrmd/GLM4.7-Distill-LFM2.5-1.2B
Source: Original Platform

README.md

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+---
+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.
+