Upload ChessLM Qwen3 Neuron model in AWS format structure

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+context_encoding_model/_tp0_bk0/graph.neff filter=lfs diff=lfs merge=lfs -text
+context_encoding_model/_tp0_bk0/model.MODULE_e80578c547275f02c0fa+ed72d204.neff filter=lfs diff=lfs merge=lfs -text
+layout_opt/graph.neff filter=lfs diff=lfs merge=lfs -text
+layout_opt/model/graph.hlo filter=lfs diff=lfs merge=lfs -text
+token_generation_model/_tp0_bk0/graph.neff filter=lfs diff=lfs merge=lfs -text
+token_generation_model/_tp0_bk0/model.MODULE_8f245c7816a398e13e79+a9d440f5.neff filter=lfs diff=lfs merge=lfs -text
+token_generation_model/_tp0_bk0/wrapped_neff.hlo filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,176 @@
+---
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- chess
+- neuron
+- aws-trainium
+- vllm
+- optimum-neuron
+- continuous-batching
+base_model: karanps/ChessLM_Qwen3
+---
+
+# ChessLM Qwen3 - Neuron Traced (AWS Format Structure)
+
+This is a Neuron-traced version of [karanps/ChessLM_Qwen3](https://huggingface.co/karanps/ChessLM_Qwen3) optimized for AWS Trainium (trn1) and Inferentia (inf2) instances using vLLM with **continuous batching enabled**.
+
+This model follows the AWS Neuron repository structure with separate directories for compiled artifacts.
+
+## Model Details
+
+- **Base Model**: Qwen3-2B fine-tuned for chess
+- **Compilation**: optimum-neuron[vllm]==0.3.0
+- **Compiler Version**: neuronxcc 2.21.33363.0
+- **Target Hardware**: AWS Trainium (trn1) / Inferentia (inf2)
+- **Precision**: BF16
+- **Tensor Parallelism**: 2 cores
+- **Batch Size**: 4 (continuous batching enabled)
+- **Max Sequence Length**: 2048
+
+## Repository Structure
+
+This repository follows the AWS Neuron format with organized directories:
+
+```
+├── context_encoding_model/
+│   └── _tp0_bk0/
+│       ├── graph.neff
+│       ├── model.MODULE_*.neff
+│       ├── model.MODULE_*.hlo_module.pb
+│       ├── compile_flags.*.json
+│       ├── neuron_config.json
+│       └── log-neuron-cc.txt
+├── token_generation_model/
+│   └── _tp0_bk0/
+│       ├── graph.neff
+│       ├── model.MODULE_*.neff
+│       ├── model.MODULE_*.hlo_module.pb
+│       ├── wrapped_neff.hlo
+│       ├── compile_flags.*.json
+│       ├── neuron_config.json
+│       └── log-neuron-cc.txt
+├── layout_opt/
+│   ├── graph.neff
+│   ├── log-neuron-cc.txt
+│   └── model/
+│       └── graph.hlo
+├── model.pt (17GB - contains compiled graphs + weights)
+├── config.json
+├── neuron_config.json
+└── tokenizer files
+```
+
+### Key Files
+
+- **context_encoding_model/**: Compiled NEFF files for processing initial prompt sequences (up to 2048 tokens)
+- **token_generation_model/**: Compiled NEFF files for autoregressive token generation
+- **layout_opt/**: Layout optimization artifacts from compilation
+- **model.pt**: Main model file containing compiled graphs and embedded weights (17GB)
+- **neuron_config.json**: Neuron compilation configuration
+
+## Difference from AWS Reference Format
+
+The AWS Neuron reference models (e.g., `aws-neuron/Qwen3-1.7B-TP2-BS8-SEQ4096`) typically have:
+- A `weights/` directory with separate safetensors files (e.g., `tp0_sharded_checkpoint.safetensors`)
+- A smaller model.pt (e.g., ~100MB) containing just the model structure
+
+**This model** has:
+- Weights embedded within model.pt (17GB)
+- An empty `weights/` directory (preserved for format compatibility)
+
+This is because Neuron-compiled models with optimum-neuron[vllm]==0.3.0 bundle weights within the compiled format. The weights are optimized and embedded in the NEFF (Neuron Executable File Format) during compilation. This is a valid alternative implementation that provides the same functionality.
+
+## Requirements
+
+```bash
+pip install optimum-neuron[vllm]==0.3.0
+pip install neuronx-distributed --extra-index-url=https://pip.repos.neuron.amazonaws.com
+```
+
+## Usage
+
+### Loading the Model
+
+```python
+from optimum.neuron import NeuronModelForCausalLM
+from transformers import AutoTokenizer
+
+# Load the model
+model = NeuronModelForCausalLM.from_pretrained("kunhunjon/ChessLM_Qwen3_Trainium_AWS_Format")
+tokenizer = AutoTokenizer.from_pretrained("kunhunjon/ChessLM_Qwen3_Trainium_AWS_Format")
+
+# Run inference
+prompt = "e2e4"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=20)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+### Hardware Requirements
+
+- AWS Trainium (trn1.32xlarge, trn1.2xlarge) or Inferentia (inf2) instances
+- At least 2 Neuron cores (as configured during tracing)
+- Minimum 32GB RAM recommended
+
+## Compilation Details
+
+This model was traced with the following parameters:
+- `batch_size=4`
+- `sequence_length=2048`
+- `num_cores=2`
+- `auto_cast_type="bf16"`
+- `continuous_batching=True`
+
+### Compilation Artifacts
+
+The separate directories contain all compilation artifacts:
+- **NEFF files**: Neuron Executable File Format - the compiled compute graphs
+- **HLO files**: High-Level Operations - intermediate representation
+- **Compilation logs**: Detailed logs from neuronx-cc compiler
+- **Metadata**: Configuration and metrics from compilation
+
+### Continuous Batching
+
+This model is compiled with **continuous batching enabled**, which allows vLLM to:
+- Process multiple requests simultaneously with dynamic batch sizes up to 4
+- Optimize throughput by batching requests with different sequence lengths
+- Reduce latency for concurrent inference workloads
+
+**Note**: On-device sampling is disabled due to a known Neuron runtime limitation when using tensor parallelism with 2 cores. Sampling is handled on the host instead.
+
+## Compilation Metrics
+
+- **Total compilation time**: ~8.1 minutes
+- **Token generation model**: 219 seconds
+- **Context encoding model**: 165 seconds
+- **Compiler**: neuronxcc 2.21.33363.0
+- **Model size**: 17GB (with embedded weights)
+
+## Model Files
+
+| File | Purpose |
+|------|---------|
+| model.pt | Main model with embedded weights (17GB) |
+| config.json | Base model configuration |
+| neuron_config.json | Neuron compilation settings |
+| tokenizer* | Tokenizer files for text processing |
+| context_encoding_model/ | Compiled graphs for prompt processing |
+| token_generation_model/ | Compiled graphs for token generation |
+| layout_opt/ | Weight layout optimization artifacts |
+
+## License
+
+This model inherits the license from the base model [karanps/ChessLM_Qwen3](https://huggingface.co/karanps/ChessLM_Qwen3).
+
+## Citation
+
+If you use this model, please cite the original ChessLM model and AWS Neuron tools.
+
+## See Also
+
+- **Sharded version**: [kunhunjon/ChessLM_Qwen3_Trainium_Sharded](https://huggingface.co/kunhunjon/ChessLM_Qwen3_Trainium_Sharded) - Model split into 9x2GB shards for easier downloading
+- **Standard version**: [kunhunjon/ChessLM_Qwen3_Trainium](https://huggingface.co/kunhunjon/ChessLM_Qwen3_Trainium) - Single model.pt file

WEIGHTS_README.md

@@ -0,0 +1,36 @@
+# Weights Information
+
+This model contains weights bundled within model.pt (17GB).
+
+In the AWS Neuron reference format, weights are typically stored separately as:
+- `weights/tp0_sharded_checkpoint.safetensors`
+- `weights/tp1_sharded_checkpoint.safetensors`
+
+To extract weights to safetensors format, you would need to:
+1. Load the model using optimum-neuron
+2. Extract the state_dict
+3. Convert to safetensors format
+4. Shard by tensor parallel rank
+
+This is currently not straightforward for compiled Neuron models as the weights
+are embedded in the compiled format.
+
+## Current Structure
+
+The model.pt file contains:
+- Compiled graphs (NEFF format)
+- Model weights (optimized for Neuron)
+- Runtime metadata
+
+The separate directories contain:
+- `context_encoding_model/`: NEFF files for context encoding
+- `token_generation_model/`: NEFF files for token generation
+- `layout_opt/`: Layout optimization artifacts
+
+##Usage
+
+Load this model using:
+```python
+from optimum.neuron import NeuronModelForCausalLM
+model = NeuronModelForCausalLM.from_pretrained("path/to/model")
+```

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
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+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

config.json

@@ -0,0 +1,69 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

context_encoding_model/_tp0_bk0/command.txt

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+neuronx-cc compile --framework=XLA model.MODULE_e80578c547275f02c0fa+ed72d204.hlo_module.pb --output model.MODULE_e80578c547275f02c0fa+ed72d204.neff --target=trn1 --auto-cast=none --model-type=transformer '--tensorizer-options=--enable-ccop-compute-overlap --cc-pipeline-tiling-factor=2 --vectorize-strided-dma ' -O2 --lnc=1 --logfile=log-neuron-cc.txt --verbose=35

context_encoding_model/_tp0_bk0/compile_flags.MODULE_e80578c547275f02c0fa+ed72d204.json

@@ -0,0 +1 @@
+["--target=trn1", "--auto-cast=none", "--model-type=transformer", "--tensorizer-options=--enable-ccop-compute-overlap --cc-pipeline-tiling-factor=2 --vectorize-strided-dma ", "-O2", "--lnc=1", "--logfile=/tmp/nxd_model/context_encoding_model/_tp0_bk0/log-neuron-cc.txt"]

context_encoding_model/_tp0_bk0/global_metric_store.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "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",
+  "extra_special_tokens": {},
+  "max_length": 512,
+  "model_max_length": 131072,
+  "pad_to_multiple_of": null,
+  "pad_token": "<|endoftext|>",
+  "pad_token_type_id": 0,
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "stride": 0,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": null
+}