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

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README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation
+- transformers
+- qwen3
+- qwen
+- ai
+- llm
+- qwen3
+- thinking
+base_model:
+- Qwen/Qwen3-1.7B
+---
+# Qwen3-1.77B-g023 (Full Precision)
+
+## Overview
+
+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.
+
+## TurboQuant-able?
+Why yes, yes it can:
+(https://github.com/g023/turboquant)
+
+## Key Result
+
+| Metric | Baseline (28 layers) | This Model (29 layers) |
+|---|---|---|
+| **Overall Score** | 85.9 / 100 | **93.6 / 100** (+7.7) |
+| **Factual Accuracy** | 7 / 9 | **9 / 9** |
+| **Avg Perplexity** | 17.71 | 19.50 |
+| **Thinking Mode** | Working | Working |
+| **Non-Thinking Mode** | Working | Working |
+
+## Architecture
+
+| Parameter | Value |
+|---|---|
+| Layers | 29 (28 original + 1 duplicated) |
+| Hidden Size | 2048 |
+| Intermediate Size | 6144 |
+| Attention Heads | 16 (query) / 8 (KV) |
+| Head Dimension | 128 |
+| Vocab Size | 151,936 |
+| Max Position Embeddings | 40,960 |
+| Total Parameters | ~1.77B |
+| Dtype | bfloat16 |
+| Tied Embeddings | Yes |
+
+## Layer Mapping
+
+```
+Source Layer  →  Output Layer
+0–20         →  0–20   (unchanged)
+21           →  21, 22 (duplicated with noise std=0.001 + depth scaling)
+22–27        →  23–28  (shifted +1)
+```
+
+## Duplication Method
+
+- **Noise injection**: Gaussian noise (std=0.001) added to duplicate layer to break symmetry
+- **Depth scaling**: Factor of √(28/29) ≈ 0.983 applied to prevent activation explosion
+- **Anchors preserved**: First layer (0) and last layer (27→28) remain unmodified
+
+## Files
+
+| File | Size | Description |
+|---|---|---|
+| `model-00001-of-00001.safetensors` | 3.3 GB | Model weights (bfloat16) |
+| `config.json` | <1 KB | Model configuration |
+| `tokenizer.json` | 11 MB | Tokenizer |
+| `tokenizer_config.json` | 10 KB | Tokenizer configuration |
+| `vocab.json` | 2.7 MB | Vocabulary |
+| `merges.txt` | 1.6 MB | BPE merges |
+| `generation_config.json` | <1 KB | Generation defaults |
+| `eval_results.json` | 1 KB | Full evaluation metrics |
+
+## Usage
+
+```python
+# Tweakable parameters
+# MODEL_PATH = "./Qwen3-BEST" # local run
+MODEL_PATH = "g023/Qwen3-1.77B-g023"
+MAX_NEW_TOKENS = 8192
+TEMPERATURE = 0.7
+DO_SAMPLE = True
+TOP_P = 0.9
+TOP_K = 50
+REPETITION_PENALTY = 1.1
+STREAMING = True  # Set to True for streaming inference
+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. "
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+import time
+
+def load_model():
+    print("Loading model...")
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_PATH,
+        device_map="auto",
+    )
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+    print("Model loaded.")
+    return model, tokenizer
+
+def inference_non_streaming(model, tokenizer, messages):
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=MAX_NEW_TOKENS,
+        temperature=TEMPERATURE,
+        do_sample=DO_SAMPLE,
+        top_p=TOP_P,
+        top_k=TOP_K,
+        repetition_penalty=REPETITION_PENALTY,
+    )
+    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+    print("Response:", response)
+    return response
+
+def inference_streaming(model, tokenizer, messages):
+    final_response = ""
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=MAX_NEW_TOKENS,
+        temperature=TEMPERATURE,
+        do_sample=DO_SAMPLE,
+        top_p=TOP_P,
+        top_k=TOP_K,
+        repetition_penalty=REPETITION_PENALTY,
+        streamer=streamer,
+    )
+
+
+    # return a final str
+    return final_response
+
+def llm_stream(model, tokenizer, conversation):
+    import time
+    start_time = time.time()
+    text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True, enable_thinking=True)
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    from io import StringIO
+    buffer = StringIO()
+    class CapturingTextStreamer(TextStreamer):
+        def __init__(self, tokenizer, buffer):
+            super().__init__(tokenizer, skip_prompt=True, skip_special_tokens=True)
+            self.buffer = buffer
+        def on_finalized_text(self, text, stream_end=False):
+            self.buffer.write(text)
+            print(text, end="", flush=True)
+    streamer = CapturingTextStreamer(tokenizer, buffer)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=MAX_NEW_TOKENS,
+        temperature=TEMPERATURE,
+        do_sample=DO_SAMPLE,
+        top_p=TOP_P,
+        top_k=TOP_K,
+        repetition_penalty=REPETITION_PENALTY,
+        streamer=streamer,
+    )
+    response = buffer.getvalue()
+
+    if "</think>" in response:
+        parts = response.rsplit("</think>", 1)
+        reasoning = parts[0].strip()
+        content = parts[1].strip()
+    else:
+        reasoning = ""
+        content = response.strip()
+    char_per_token = 3.245
+    reasoning_tokens = round(len(reasoning) / char_per_token)
+    content_tokens = round(len(content) / char_per_token)
+    total_tokens = reasoning_tokens + content_tokens
+    time_taken = time.time() - start_time
+    ret_dict = {
+        "reasoning": reasoning,
+        "content": content,
+        "usage": {
+            "reasoning_tokens": reasoning_tokens,
+            "content_tokens": content_tokens,
+            "total_tokens": total_tokens,
+        },
+        "time_taken": time_taken,
+    }
+    return ret_dict
+
+if __name__ == "__main__":
+    model, tokenizer = load_model()
+    messages = [{"role": "user", "content": INPUT_MESSAGE}]
+    ret = llm_stream(model, tokenizer, messages)
+    print("Result dict:", ret)
+
+    # output tokens per second by taking total_tokens and time_taken
+    if ret["usage"]["total_tokens"] > 0 and ret["time_taken"] > 0:
+        tps = ret["usage"]["total_tokens"] / ret["time_taken"]
+        print(f"Tokens per second: {tps:.2f}")
+```
+
+## Base Model
+
+- **Model**: Qwen/Qwen3-1.7B
+- **Architecture**: Qwen3ForCausalLM (decoder-only transformer with GQA)
+- **License**: Apache 2.0

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 29,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 29,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

eval_results.json

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+{
+  "label": "config_u_layer_21 (29 layers)",
+  "path": "./Qwen3-ITER/config_u_layer_21",
+  "num_layers": 29,
+  "total_params": 1770910976,
+  "factual_pass": 9,
+  "factual_total": 9,
+  "factual_rate": 1.0,
+  "completion_coherent": 2,
+  "completion_total": 2,
+  "avg_perplexity": 19.501575589179993,
+  "min_perplexity": 5.643493175506592,
+  "max_perplexity": 97.80669403076172,
+  "repetition_ratio": 0.6777777777777778,
+  "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",
+  "thinking_mode_ok": true,
+  "non_thinking_mode_ok": true,
+  "overall_score": 93.5778752822502,
+  "config_name": "config_u_layer_21",
+  "dup_start": 21,
+  "dup_end": 21,
+  "dup_count": 1
+}

generation_config.json

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+{
+    "bos_token_id": 151643,
+    "do_sample": true,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "pad_token_id": 151643,
+    "temperature": 0.6,
+    "top_k": 20,
+    "top_p": 0.95,
+    "transformers_version": "4.51.0"
+}

model-00001-of-00001.safetensors

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model.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "g023/Qwen3-1.77B-g023",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("g023/Qwen3-1.77B-g023")
+
+# Non-thinking mode
+messages = [{"role": "user", "content": "What is the capital of France?"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+
+# Thinking mode
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=500)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))

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+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "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
+}