Upload LlamaForCausalLM

README.md

@@ -0,0 +1,201 @@
+---
+library_name: transformers
+tags: []
+---
+
+# 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
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+

config.json

@@ -0,0 +1,63 @@
+{
+  "_name_or_path": "/model",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 100000,
+  "eos_token_id": 100001,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "batch_size": 1,
+    "bits": 4,
+    "block_name_to_quantize": null,
+    "cache_block_outputs": true,
+    "damp_percent": 0.1,
+    "dataset": [
+      "The distance between two stars is 6.52 \u00d7 10^5 light years. What is the distance between the two stars in parsecs? (1 parsec = 3.26 light years)\nAnswer Choices: (A) 2 \u00d7 10^5 (B) 4 \u00d7 10^6 (C) 5 \u00d7 10^7 (D) 7 \u00d7 10^7 (E) 9 \u00d7 10^8Let's think about the multi-choice question.\n6.52 \u00d7 10^5 ly / (3.26 ly/parsec) = 2 x 10^5 persec\nThe answer is A.",
+      "How many ways can the letters in the word COMMON be arranged?\nAnswer Choices: (A) 6 (B) 30 (C) 90 (D) 120 (E) 180Let's solve the multi-choice question step by step.\nAccording to the above the # of permutations of 6 letters COMMON out of which 2 O's and 2 M's are identical is 6!2!\u22172!=180\nThe answer is E.",
+      "A team of six entered for a shooting competition. The best marks man scored 85 points. If he had scored 92 points, the average scores for. The team would have been 84. How many points altogether did the team score?\nAnswer Choices: (A) 288 (B) 497 (C) 168 (D) 127 (E) 664 Let's program in Python in the response.answers = ['A', 'B', 'C', 'D', 'E']\n# If the best marksman had scored 92 points, the total score would have been 84 * 6 = 504\n# But he actually scored 85 points, so the actual total score is 504 - 92 + 85\nactual_total_score = 504 - 92 + 85\noptions = [288, 497, 168, 127, 664]\nindex = options.index(actual_total_score)\nprint(answers[index])",
+      "A psychiatrist has 4 patients that need 25 sessions in total. One of the patients needs 6 sessions. Another patient needs 5 more than that. How many sessions would the remaining patients need?The second patient needs 6+5 = 11 sessions\n25-11-6 = 8 sessions\nThe answer is 8",
+      "The radius of a wheel is 22.4 cm. What is the distance covered by the wheel in making 500 resolutions?\nAnswer Choices: (A) 187 m (B) 704 m (C) 179 m (D) 127 m (E) 297 m Let's write a Python program.radius = 22.4\nresolutions = 500\n# calculate the circumference of the wheel\ncircumference = 2 * 3.14 * radius\n# calculate the distance covered by the wheel in making 500 resolutions\ndistance = circumference * resolutions\nprint(distance)",
+      "Let G be a group of order 35. What can be said about G?  Answer Choices: (A) G must be abelian. (B) G must be cyclic. (C) G must be a direct product of cyclic groups. (D) G cannot be cyclic.By the Fundamental Theorem of Finite Abelian Groups, any group of order 35, which is the product of two distinct prime numbers (5 and 7), must be a direct product of cyclic groups. Hence, option (C) is correct. Let's check each option: (A) G must be abelian: It is not necessarily true that G must be abelian. The statement would be true if G were of prime order, but that's not the case here. (B) G must be cyclic: Again, it's not necessarily true that G must be cyclic. A group is cyclic if it is generated by a single element, but this isn't guaranteed for a group of order 35. (C) G must be a direct product of cyclic groups: This is correct. The Fundamental Theorem of Finite Abelian Groups tells us that a group of order 35 must be isomorphic to a direct product of cyclic groups. (D) G cannot be cyclic: This is not necessarily true. It's possible for G to be cyclic, although it's not guaranteed. The answer is B.",
+      "At a pool party, there are 4 pizzas cut into 12 slices each.  If the guests eat 39 slices, how many slices are left? Let's write a Python program.# define the initial number of slices\ntotal_slices = 4 * 12\n# define the number of slices eaten\neaten_slices = 39\n# calculate the number of slices left\nleft_slices = total_slices - eaten_slices\n# print the result\nprint(left_slices)",
+      "Noel bakes 4 dozen donuts for his class. There are 30 students in class, but only 80% like donuts. How many donuts does each student who likes donuts get to eat? Please write a program to solve it# define the variables\ntotal_donuts = 4 * 12  # since a dozen is 12\ntotal_students = 30\npercentage_like_donuts = 0.8  # 80%\n\n# calculate the number of students who like donuts\nstudents_like_donuts = total_students * percentage_like_donuts\n\n# calculate the number of donuts each student who likes donuts gets\ndonuts_per_student = total_donuts / students_like_donuts\n\n# print the result\nprint(donuts_per_student)",
+      "Mr. Thomas invested an amount of Rs. 13,900 divided in two different schemes A and B at the simple interest rate of 14% p.a. and 11% p.a. respectively. If the total amount of simple interest earned in 2 years be Rs. 3508, what was the amount invested in scheme B?\nAnswer Choices: (A) 6400 (B) 2778 (C) 2699 (D) 2789 (E) 1279Let's solve the multi-choice question step by step.\nLet the sum invested in scheme A be Rs. x and that in scheme B be Rs. (13900 - x). Then,\n(x * 14 * 2)/100 + [(13900 - x) * 11 * 2]/100 = 3508\n28x - 22x = 350800 - (13900 * 22)\n6x = 45000 => x = 7500\nSo, sum invested in scheme B = (13900 - 7500) = Rs. 6400.\nThe answer is A",
+      "louie takes out a 3 - month loan of $ 1000 . the lender charges him 10 % interest per month compounded monthly . the terms of the loan state that louie must repay the loan in 3 equal monthly payments . to the nearest dollar , how much does louis have to pay each month ? Let's write a program.n0 = 3.0\nn1 = 1000.0\nn2 = 10.0\nn3 = 3.0\nt0 = n2 / 100.0\nt1 = n0 * n1\nt2 = t0 * t1\nt3 = t0 * t2\nt4 = t2 + t3\nt5 = t4 + 1.0\nt6 = n1 + t5\nt7 = t5 / 100.0\nanswer = t6 / t7\nprint(answer)"
+    ],
+    "desc_act": false,
+    "exllama_config": {
+      "version": 1
+    },
+    "group_size": 128,
+    "max_input_length": null,
+    "model_seqlen": null,
+    "module_name_preceding_first_block": null,
+    "modules_in_block_to_quantize": null,
+    "pad_token_id": null,
+    "quant_method": "gptq",
+    "sym": true,
+    "tokenizer": null,
+    "true_sequential": true,
+    "use_cuda_fp16": false,
+    "use_exllama": true
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.2",
+  "use_cache": true,
+  "vocab_size": 102400
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 100000,
+  "eos_token_id": 100001,
+  "transformers_version": "4.37.2"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b1c06cd24dea41aa6a815e71f64df339d971b355a321687a6733b26a65b975f
+size 4839372512