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Model: g023/Qwen3-1.77B-g023 Source: Original Platform
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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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