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Model: g023/Qwen3-1.77B-g023 Source: Original Platform
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211
README.md
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README.md
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---
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation
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- transformers
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- qwen3
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- qwen
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- ai
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- llm
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- qwen3
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- thinking
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base_model:
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- Qwen/Qwen3-1.7B
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---
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# Qwen3-1.77B-g023 (Full Precision)
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## Overview
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This is an optimized variant of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) created by duplicating **layer 21** to produce a 29-layer model (up from the original 28). The optimal duplication point was found through 5 rounds of iterative testing across layers 9–25, evaluating factual accuracy, perplexity, repetition, and thinking mode functionality.
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## TurboQuant-able?
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Why yes, yes it can:
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(https://github.com/g023/turboquant)
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## Key Result
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| Metric | Baseline (28 layers) | This Model (29 layers) |
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|---|---|---|
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| **Overall Score** | 85.9 / 100 | **93.6 / 100** (+7.7) |
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| **Factual Accuracy** | 7 / 9 | **9 / 9** |
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| **Avg Perplexity** | 17.71 | 19.50 |
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| **Thinking Mode** | Working | Working |
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| **Non-Thinking Mode** | Working | Working |
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## Architecture
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| Parameter | Value |
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|---|---|
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| Layers | 29 (28 original + 1 duplicated) |
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| Hidden Size | 2048 |
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| Intermediate Size | 6144 |
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| Attention Heads | 16 (query) / 8 (KV) |
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| Head Dimension | 128 |
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| Vocab Size | 151,936 |
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| Max Position Embeddings | 40,960 |
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| Total Parameters | ~1.77B |
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| Dtype | bfloat16 |
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| Tied Embeddings | Yes |
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## Layer Mapping
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```
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Source Layer → Output Layer
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0–20 → 0–20 (unchanged)
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21 → 21, 22 (duplicated with noise std=0.001 + depth scaling)
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22–27 → 23–28 (shifted +1)
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```
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## Duplication Method
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- **Noise injection**: Gaussian noise (std=0.001) added to duplicate layer to break symmetry
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- **Depth scaling**: Factor of √(28/29) ≈ 0.983 applied to prevent activation explosion
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- **Anchors preserved**: First layer (0) and last layer (27→28) remain unmodified
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## Files
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| File | Size | Description |
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|---|---|---|
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| `model-00001-of-00001.safetensors` | 3.3 GB | Model weights (bfloat16) |
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| `config.json` | <1 KB | Model configuration |
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| `tokenizer.json` | 11 MB | Tokenizer |
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| `tokenizer_config.json` | 10 KB | Tokenizer configuration |
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| `vocab.json` | 2.7 MB | Vocabulary |
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| `merges.txt` | 1.6 MB | BPE merges |
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| `generation_config.json` | <1 KB | Generation defaults |
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| `eval_results.json` | 1 KB | Full evaluation metrics |
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## Usage
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```python
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# Tweakable parameters
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# MODEL_PATH = "./Qwen3-BEST" # local run
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MODEL_PATH = "g023/Qwen3-1.77B-g023"
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MAX_NEW_TOKENS = 8192
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TEMPERATURE = 0.7
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DO_SAMPLE = True
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TOP_P = 0.9
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TOP_K = 50
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REPETITION_PENALTY = 1.1
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STREAMING = True # Set to True for streaming inference
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INPUT_MESSAGE = "You are completing the next step in a task to create an arcade game in javascript. Your available tools are rationalize, red_green_tdd, and create_plan. Synthesize their output when reasoning. "
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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import time
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def load_model():
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print("Loading model...")
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_PATH,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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print("Model loaded.")
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return model, tokenizer
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def inference_non_streaming(model, tokenizer, messages):
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=MAX_NEW_TOKENS,
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temperature=TEMPERATURE,
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do_sample=DO_SAMPLE,
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top_p=TOP_P,
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top_k=TOP_K,
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repetition_penalty=REPETITION_PENALTY,
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)
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response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print("Response:", response)
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return response
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def inference_streaming(model, tokenizer, messages):
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final_response = ""
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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outputs = model.generate(
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**inputs,
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max_new_tokens=MAX_NEW_TOKENS,
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temperature=TEMPERATURE,
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do_sample=DO_SAMPLE,
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top_p=TOP_P,
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top_k=TOP_K,
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repetition_penalty=REPETITION_PENALTY,
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streamer=streamer,
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)
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# return a final str
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return final_response
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def llm_stream(model, tokenizer, conversation):
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import time
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start_time = time.time()
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text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True, enable_thinking=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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from io import StringIO
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buffer = StringIO()
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class CapturingTextStreamer(TextStreamer):
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def __init__(self, tokenizer, buffer):
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super().__init__(tokenizer, skip_prompt=True, skip_special_tokens=True)
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self.buffer = buffer
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def on_finalized_text(self, text, stream_end=False):
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self.buffer.write(text)
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print(text, end="", flush=True)
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streamer = CapturingTextStreamer(tokenizer, buffer)
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outputs = model.generate(
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**inputs,
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max_new_tokens=MAX_NEW_TOKENS,
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temperature=TEMPERATURE,
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do_sample=DO_SAMPLE,
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top_p=TOP_P,
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top_k=TOP_K,
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repetition_penalty=REPETITION_PENALTY,
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streamer=streamer,
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)
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response = buffer.getvalue()
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if "</think>" in response:
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parts = response.rsplit("</think>", 1)
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reasoning = parts[0].strip()
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content = parts[1].strip()
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else:
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reasoning = ""
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content = response.strip()
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char_per_token = 3.245
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reasoning_tokens = round(len(reasoning) / char_per_token)
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content_tokens = round(len(content) / char_per_token)
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total_tokens = reasoning_tokens + content_tokens
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time_taken = time.time() - start_time
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ret_dict = {
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"reasoning": reasoning,
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"content": content,
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"usage": {
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"reasoning_tokens": reasoning_tokens,
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"content_tokens": content_tokens,
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"total_tokens": total_tokens,
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},
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"time_taken": time_taken,
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}
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return ret_dict
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if __name__ == "__main__":
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model, tokenizer = load_model()
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messages = [{"role": "user", "content": INPUT_MESSAGE}]
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ret = llm_stream(model, tokenizer, messages)
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print("Result dict:", ret)
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# output tokens per second by taking total_tokens and time_taken
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if ret["usage"]["total_tokens"] > 0 and ret["time_taken"] > 0:
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tps = ret["usage"]["total_tokens"] / ret["time_taken"]
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print(f"Tokens per second: {tps:.2f}")
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```
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## Base Model
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- **Model**: Qwen/Qwen3-1.7B
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- **Architecture**: Qwen3ForCausalLM (decoder-only transformer with GQA)
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- **License**: Apache 2.0
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30
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"max_position_embeddings": 40960,
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"max_window_layers": 29,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 29,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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eval_results.json
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eval_results.json
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{
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"label": "config_u_layer_21 (29 layers)",
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"path": "./Qwen3-ITER/config_u_layer_21",
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"num_layers": 29,
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"total_params": 1770910976,
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"factual_pass": 9,
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"factual_total": 9,
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"factual_rate": 1.0,
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"completion_coherent": 2,
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"completion_total": 2,
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"avg_perplexity": 19.501575589179993,
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"min_perplexity": 5.643493175506592,
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"max_perplexity": 97.80669403076172,
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"repetition_ratio": 0.6777777777777778,
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"repetition_text": "young girl named Lila who was curious and adventurous. She loved to explore and discover new things. One day, while exploring a hidden forest, she stumbled upon a magical tree that had grown in a unique way. The tree was not just any ordinary tree; it was a tree of dreams, and it had the power to grant wishes. But there was a catch - the wish had to be made in a specific way, and the tree would only grant the wish if the wish was made with pure",
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"thinking_mode_ok": true,
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"non_thinking_mode_ok": true,
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"overall_score": 93.5778752822502,
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"config_name": "config_u_layer_21",
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"dup_start": 21,
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"dup_end": 21,
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"dup_count": 1
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "4.51.0"
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}
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151388
merges.txt
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151388
merges.txt
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File diff suppressed because it is too large
Load Diff
3
model-00001-of-00001.safetensors
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model-00001-of-00001.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b7768d271085660b303679004a486c9c8e839099a48a24c8e4b9226f24f5c9d2
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size 3541858848
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22
model.py
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model.py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"g023/Qwen3-1.77B-g023",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("g023/Qwen3-1.77B-g023")
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# Non-thinking mode
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messages = [{"role": "user", "content": "What is the capital of France?"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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# Thinking mode
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=500)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
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328
model.safetensors.index.json
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328
model.safetensors.index.json
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{
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BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user