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cld2labs/TinyLlama-1.1B-Chat-v1.0 (#94)
* feat: add TinyLlama-1.1B-Chat-v1.0 model card and deployment guide for Dell EI Signed-off-by: arpannookala-12 <ganesh.arpan.nookala@cloud2labs.com> * update tinyllama deployment guide * Enable ingress and update deployment instructions * update tinyllama deployment.md * update tinyllama deployment.md * update tinyllama deployment.md * Remove README.md from model-deployment folder --------- Signed-off-by: arpannookala-12 <ganesh.arpan.nookala@cloud2labs.com> Co-authored-by: Harika <codewith3@gmail.com> Co-authored-by: Harika <harika.devulapally@cloud2labs.com>
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‎third_party/Dell/model-deployment/TinyLlama-1.1B-Chat-v1.0/deployment.md‎

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## Step 1: Prerequisites to Deploy TinyLlama Model on Xeon with Keycloak
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Ensure the Enterprise Inference stack with Keycloak is already deployed before proceeding.
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Edit `core/scripts/generate-token.sh` and set your values before sourcing it:
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| Variable | Description |
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| ------------------------- | ------------------------------------------------------------------------ |
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| `BASE_URL` | Hostname of your cluster (e.g. `api.example.com`), without `https://` |
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| `KEYCLOAK_ADMIN_USERNAME` | Keycloak admin username |
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| `KEYCLOAK_PASSWORD` | Keycloak admin password |
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| `KEYCLOAK_CLIENT_ID` | Keycloak client ID configured during EI deployment |
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Then run:
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```bash
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export HUGGING_FACE_HUB_TOKEN="your_token_here"
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cd ~/Enterprise-Inference
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source core/scripts/generate-token.sh
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```
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This exports: `BASE_URL`, `KEYCLOAK_CLIENT_ID`, `KEYCLOAK_CLIENT_SECRET`, and `TOKEN`.
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## Step 2: Deploy Tinyllama-1.1b-chat-v1.0 Model
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```bash
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helm install tinyllama-1-1b-cpu ./core/helm-charts/vllm \
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--values ./core/helm-charts/vllm/xeon-values.yaml \
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--set LLM_MODEL_ID="TinyLlama/TinyLlama-1.1B-Chat-v1.0" \
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--set global.HUGGINGFACEHUB_API_TOKEN="$HUGGING_FACE_HUB_TOKEN" \
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--set ingress.enabled=true \
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--set ingress.secretname="${BASE_URL}" \
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--set ingress.host="${BASE_URL}" \
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--set oidc.client_id="$KEYCLOAK_CLIENT_ID" \
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--set oidc.client_secret="$KEYCLOAK_CLIENT_SECRET" \
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--set apisix.enabled=true \
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--set tensor_parallel_size="1" \
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--set pipeline_parallel_size="1"
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```
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## Step 3: Verify the Deployment
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```bash
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kubectl get pods
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kubectl get apisixroutes
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```
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Expected Output:
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```
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NAME READY STATUS RESTARTS
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keycloak-0 1/1 Running 0
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keycloak-postgresql-0 1/1 Running 0
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tinyllama-1-1b-cpu-vllm-<hash>-<hash> 1/1 Running 0
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```
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> Note: The pod name suffix `<hash>-<hash>` is auto-generated by Kubernetes and will differ on each deployment. Ensure all pods show `1/1 Running`.
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```
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NAME HOSTS
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tinyllama-1-1b-cpu-vllm-apisixroute api.example.com
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```
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## Step 4: Test the Deployed Model
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```bash
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curl -k https://${BASE_URL}/tinyLlama-1.1B-Chat-v1.0-vllmcpu/v1/completions \
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-X POST \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer $TOKEN" \
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-d '{
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"model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"prompt": "What is Deep Learning?",
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"max_tokens": 25,
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"temperature": 0
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}'
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```
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If successful, the model will return a completion response.
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## To undeploy the model
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```bash
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helm uninstall tinyllama-1-1b-cpu
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```
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## Parameters
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| Parameter | Description |
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| --------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
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| `--set LLM_MODEL_ID="TinyLlama/TinyLlama-1.1B-Chat-v1.0"` | Defines the target model from **Hugging Face** to deploy. |
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| `--set global.HUGGINGFACEHUB_API_TOKEN="..."` | Authenticates access to gated or private Hugging Face models. Replace with your own secure token. |
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| `--set ingress.enabled=true` | Enables Kubernetes **Ingress** to expose the model service externally. |
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| `--set ingress.host="replace-ingress"` | Public hostname or FQDN for the inference endpoint (maps to your Ingress controller IP). |
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| `--set ingress.secretname="replace-secret"` | Kubernetes **TLS Secret** used for HTTPS termination at the ingress layer. |
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| `--set oidc.client_id="..."` | Keycloak OIDC client ID used for token-based authentication. |
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| `--set oidc.client_secret="..."` | Keycloak OIDC client secret corresponding to the client ID. |
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| `--set apisix.enabled=true` | Enables **APISIX** as the API gateway for routing and authentication. |
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| `--set tensor_parallel_size="1"` | Number of tensor parallel workers. Set to the number of available CPUs/GPUs per node. |
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| `--set pipeline_parallel_size="1"` | Number of pipeline parallel stages. Typically `1` for single-node deployments. |
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‎third_party/Dell/model-deployment/TinyLlama-1.1B-Chat-v1.0/model-card.md‎

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# TinyLlama-1.1B-Chat-v1.0
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This model uses TinyLlama-1.1B-Chat-v1.0, a compact large language model developed by the TinyLlama Project team. It is a chat-tuned variant of the TinyLlama 1.1B base model, optimized for instruction-following, conversational AI, and lightweight reasoning tasks. Despite its small size, TinyLlama delivers strong performance for edge AI, embedded systems, rapid prototyping, and cost-efficient inference scenarios.
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For full details including model specifications, licensing, intended use, safety guidance, and example prompts, please visit the official Hugging Face page: **Official Hugging Face Page**
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https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
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This model provides inference services only; weights are hosted by Hugging Face under the Apache 2.0 License.
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Ensure compliance with the Apache 2.0 License terms before using this model.
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### Model Attribution
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**Developer:** TinyLlama Project
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**purpose:** Lightweight instruction-tuned conversational AI
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**Sizes/Variants:** 1.1B parameters
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**Modalities:** Text → Natural Language
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**Parameter Size:** 1.1 Billion
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**Max Context:** ~2K tokens
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**License:** Apache 2.0 (commercial-friendly)
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### Usage Notice
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**By using this model, you agree that:**
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- Inputs and outputs are processed by the TinyLlama-1.1B-Chat-v1.0 model under the Apache 2.0 license.
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- You are responsible for validating outputs before production use.
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- This model should not be used for generating malicious, deceptive, or unsafe content.
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- Outputs may contain inaccuracies and must be reviewed for correctness and compliance.
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### Intended Applications
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- Lightweight chatbots and virtual assistants
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- Edge AI and on-device inference
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- Rapid prototyping and AI experimentation
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- CPU-based conversational agents
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- Educational tools and demos
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- RAG-based document assistants for low-resource environments
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- Dev/test automation helpers
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### Limitations
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- Limited reasoning depth compared to large models (7B+)
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- Reduced long-context understanding
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- Not suitable for complex multi-step logic or heavy code generation
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- May hallucinate or oversimplify responses
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- Not designed for safety-critical or regulated decision systems
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### References
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TinyLlama Project — https://github.com/jzhang38/TinyLlama
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Hugging Face Model Page — https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0

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