Upload Qwen2ForCausalLM

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+easydel-model.parameters filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,199 +1,80 @@
+
 ---
-library_name: transformers
-tags: []
+tags:
+- EasyDeL
+- Qwen2ForCausalLM
+- safetensors
+- TPU
+- GPU
+- XLA
+- Flax
 ---
+# EasyDeL/GRPO-Qwen2-0.5b-instruct
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
+[![EasyDeL](https://img.shields.io/badge/🤗_EasyDeL-0.1.0-blue.svg)](https://github.com/erfanzar/EasyDeL)
+[![Model Type](https://img.shields.io/badge/Model_Type-Qwen2ForCausalLM-green.svg)](https://github.com/erfanzar/EasyDeL)
 
-[More Information Needed]
+A model implemented using the EasyDeL framework, designed to deliver optimal performance for large-scale natural language processing tasks.
 
-### Compute Infrastructure
+## Overview
 
-[More Information Needed]
+EasyDeL provides an efficient, highly-optimized, and customizable machine learning model compatible with both GPU and TPU environments. Built with JAX, this model supports advanced features such as sharded model parallelism, making it suitable for distributed training and inference and customized kernels.
 
-#### Hardware
+## Features
 
-[More Information Needed]
 
-#### Software
+- **Efficient Implementation**: Built with JAX/Flax for high-performance computation.
+- **Multi-Device Support**: Optimized to run on TPU, GPU, and CPU environments for sharding model over 2^(1-1000+) of devices.
+- **Sharded Model Parallelism**: Supports model parallelism across multiple devices for scalability.
+- **Customizable Precision**: Allows specification of floating-point precision for performance optimization.
 
-[More Information Needed]
 
-## Citation [optional]
+## Installation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+To install EasyDeL, simply run:
 
-**BibTeX:**
+```bash
+pip install easydel
+```
 
-[More Information Needed]
+## Usage
 
-**APA:**
+### Loading the Pre-trained Model
 
-[More Information Needed]
+To load a pre-trained version of the model with EasyDeL:
 
-## Glossary [optional]
+```python
+from easydel import AutoEasyDeLModelForCausalLM
+from jax import numpy as jnp, lax
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+max_length = None # can be set to use lower memory for caching
 
-[More Information Needed]
+# Load model and parameters
+model = AutoEasyDeLModelForCausalLM.from_pretrained(
+    "EasyDeL/GRPO-Qwen2-0.5b-instruct",
+    config_kwargs=ed.EasyDeLBaseConfigDict(
+        use_scan_mlp=False,
+        attn_dtype=jnp.float16,
+        freq_max_position_embeddings=max_length,
+        mask_max_position_embeddings=max_length,
+        attn_mechanism=ed.AttentionMechanisms.FLASH_ATTN2
+    ), 
+    dtype=jnp.float16,
+    param_dtype=jnp.float16,
+    precision=lax.Precision("fastest"),
+    auto_shard_model=True, 
+)
+```
 
-## More Information [optional]
+## Supported Tasks
 
-[More Information Needed]
 
-## Model Card Authors [optional]
+[Need more information]
 
-[More Information Needed]
+ 
+## Limitations
 
-## Model Card Contact
 
-[More Information Needed]
+- **Hardware Dependency**: Performance can vary significantly based on the hardware used.
+- **JAX/Flax Setup Required**: The environment must support JAX/Flax for optimal use.
+- **Experimental Features**: Some features (like custom kernel usage or ed-ops) may require additional configuration and tuning.

config.json

@@ -0,0 +1,96 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_axis_name": "sp",
+  "attention_dropout": 0.0,
+  "attn_mechanism": "vanilla",
+  "axis_dims": [
+    1,
+    1,
+    -1,
+    1
+  ],
+  "axis_names": [
+    "dp",
+    "fsdp",
+    "tp",
+    "sp"
+  ],
+  "backend": null,
+  "bits": null,
+  "blocksize_b": 1,
+  "blocksize_k": 128,
+  "blocksize_q": 128,
+  "bos_token_id": 151643,
+  "easy_method": "train",
+  "embd_pdrop": 0.0,
+  "eos_token_id": 151645,
+  "fcm_max_ratio": 0.0,
+  "fcm_min_ratio": 0.0,
+  "flash_attention_backward_pass_impl": "triton",
+  "freq_max_position_embeddings": 2048,
+  "gradient_checkpointing": "",
+  "hardware_abstraction": false,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "kv_cache_quantization_blocksize": 64,
+  "kv_cache_quantization_method": "None",
+  "kv_cache_sharding_sequence_axis_name": "sp",
+  "mask_max_position_embeddings": 2048,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "number_rep_kv": 1,
+  "pallas_k_block_size": null,
+  "pallas_m_block_size": null,
+  "pallas_n_block_size": null,
+  "partition_axis": [
+    [
+      "fsdp",
+      "dp"
+    ],
+    "sp",
+    "sp",
+    "tp",
+    "sp",
+    "tp",
+    null,
+    null,
+    null,
+    null,
+    "tp",
+    "sp",
+    null
+  ],
+  "platform": null,
+  "pretraining_tp": 1,
+  "quantization_blocksize": 64,
+  "quantization_method": "None",
+  "quantization_pattern": ".*",
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "scan_attention_layers": false,
+  "scan_layers": true,
+  "scan_mlp_chunk_size": 1024,
+  "scan_ring_attention": true,
+  "shard_attention_computation": true,
+  "sliding_window": 32768,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.47.1",
+  "use_cache": true,
+  "use_scan_mlp": false,
+  "use_sharded_kv_caching": false,
+  "use_sharding_constraint": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

easydel-model.parameters

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f00a1106fffe9c2a726462959d295495a83efc6bdf012d34bb71e3c76dd7a520
+size 1260367416

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.47.1"
+}