initial commit to repo

README.md

@@ -1,3 +1,110 @@
 ---
 license: apache-2.0
 ---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+bling-falcon-1b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series, instruct trained on top of a falcon-rw-1b base model
+
+BLING models are fine-tuned with distilled high-quality custom instruct datasets, targeted at a specific subset of instruct tasks with 
+the objective of providing a high-quality Instruct model that is 'inference-ready' on a CPU laptop even 
+without using any advanced quantization optimizations.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** llmware
+- **Model type:** GPTNeoX instruct-trained decoder
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** tiiuae/falcon-rw-1b
+
+## 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. -->
+
+The intended use of BLING models is two-fold:
+
+1.  Provide high-quality Instruct models that can run on a laptop for local testing.  We have found it extremely useful when building a
+   proof-of-concept, or working with sensitive enterprise data that must be closely guarded, especially in RAG use cases.
+
+2.  Push the state of the art for smaller Instruct-following models in the sub-7B parameter range, especially 1B-3B, as single-purpose
+    automation tools for specific tasks through targeted fine-tuning datasets and focused "instruction" tasks.
+
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+BLING is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
+legal and regulatory industries with complex information sources.  Rather than try to be "all things to all people," BLING models try to focus on a narrower set of Instructions more suitable to a ~1B parameter GPT model.
+
+BLING is ideal for rapid prototyping, testing, and the ability to perform an end-to-end workflow locally on a laptop without
+having to send sensitive information over an Internet-based API.
+
+The first BLING models have been trained for common RAG scenarios, specifically:   question-answering, key-value extraction, and basic summarization as the core instruction types
+without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+1.  BLING is not designed for 'chat-bot' or 'consumer-oriented' applications. 
+
+2.  BLING is not optimal for most production applications, other than simple and highly specific use cases.
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+BLING has not been designed for end consumer-oriented applications, and there has not been any focus in training on safeguards to mitigate potential bias.  We would strongly discourage any use of BLING for any 'chatbot' use case.
+
+
+## How to Get Started with the Model
+
+The fastest way to get started with BLING is through direct import in transformers:
+
+from transformers import AutoTokenizer, AutoModelForCausalLM  
+tokenizer = AutoTokenizer.from_pretrained("llmware/bling-falcon-1b-0.1")  
+model = AutoModelForCausalLM.from_pretrained("llmware/bling-falcon-1b-0.1")  
+
+
+The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:
+
+full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\: "
+
+The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:
+
+1.  Text Passage Context, and
+2.  Specific question or instruction based on the text passage
+
+To get the best results, package "my_prompt" as follows:
+
+my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
+
+
+## Citation [optional]
+
+This BLING models was built on top of a Falcon model base - for more information, please see the paper referenced below:
+
+{
+Title:  "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only"
+Authors:  Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Allessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, Julien Launay
+Publication Date: June 1, 2023
+} 
+
+
+
+## Model Card Contact
+
+Darren Oberst & llmware team
+
+Please reach out anytime if you are interested in this project and would like to participate and work with us!
+
+
+

config.json

@@ -0,0 +1,96 @@
+{
+  "model_name": "bling-falcon-1b-0.1",
+  "description": "Instruct train fine-tuning using distilled knowledge based critical reading tasks training dataset",
+  "training_timestamp": "Sat Oct 7 09:46:53 2023",
+  "training_comments": "falcon-rw-1b-base",
+  "_name_or_path": "tiiuae/falcon-rw-1b",
+  "model_type": "falcon",
+  "vocab_size": 50304,
+  "hidden_size": 2048,
+  "num_hidden_layers": 24,
+  "num_attention_heads": 32,
+  "layer_norm_epsilon": 1e-05,
+  "initializer_range": 0.02,
+  "use_cache": true,
+  "hidden_dropout": 0.0,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "num_kv_heads": 32,
+  "alibi": true,
+  "new_decoder_architecture": false,
+  "multi_query": false,
+  "parallel_attn": false,
+  "bias": true,
+  "return_dict": true,
+  "output_hidden_states": false,
+  "output_attentions": false,
+  "torchscript": false,
+  "torch_dtype": "bfloat16",
+  "use_bfloat16": false,
+  "tf_legacy_loss": false,
+  "pruned_heads": {},
+  "tie_word_embeddings": true,
+  "is_encoder_decoder": false,
+  "is_decoder": false,
+  "cross_attention_hidden_size": null,
+  "add_cross_attention": false,
+  "tie_encoder_decoder": false,
+  "max_length": 20,
+  "min_length": 0,
+  "do_sample": false,
+  "early_stopping": false,
+  "num_beams": 1,
+  "num_beam_groups": 1,
+  "diversity_penalty": 0.0,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "typical_p": 1.0,
+  "repetition_penalty": 1.0,
+  "length_penalty": 1.0,
+  "no_repeat_ngram_size": 0,
+  "encoder_no_repeat_ngram_size": 0,
+  "bad_words_ids": null,
+  "num_return_sequences": 1,
+  "chunk_size_feed_forward": 0,
+  "output_scores": false,
+  "return_dict_in_generate": false,
+  "forced_bos_token_id": null,
+  "forced_eos_token_id": null,
+  "remove_invalid_values": false,
+  "exponential_decay_length_penalty": null,
+  "suppress_tokens": null,
+  "begin_suppress_tokens": null,
+  "architectures": [
+    "FalconForCausalLM"
+  ],
+  "finetuning_task": null,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "tokenizer_class": null,
+  "prefix": null,
+  "pad_token_id": null,
+  "sep_token_id": null,
+  "decoder_start_token_id": null,
+  "task_specific_params": null,
+  "problem_type": null,
+  "_name_or_path": "tiiuae/falcon-rw-1b",
+  "transformers_version": "4.28.1",
+  "apply_residual_connection_post_layernorm": false,
+  "auto_map": {
+    "AutoConfig": "configuration_falcon.FalconConfig",
+    "AutoModel": "modeling_falcon.FalconModel",
+    "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",
+    "AutoModelForQuestionAnswering": "modeling_falcon.FalconForQuestionAnswering",
+    "AutoModelForCausalLM": "modeling_falcon.FalconForCausalLM"
+  },
+  "trained": "custom training"
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6ea6aeb7de3e54bd5d183c789a2785b607f67d52f9a0f3924bb3ec085e53aa0
+size 5246615285